State of the art for genetic algorithms and beyond in water resources planning and management

Journal of Water Resources Planning and Management

Volumn 136, Issue 4, 2010, Pages 412-432

  Nicklow, John ^a   Reed, Patrick ^b   Savic, Dragan ^c   Dessalegne, Tibebe ^d   Harrell, Laura ^e   Chan Hilton, Amy ^f   Karamouz, Mohammad ^g   Minsker, Barbara ^h   Ostfeld, Avi ⁱ   Singh, Abhishek ^j   Zechman, Emily ^k  

^a Southern Illinois University Carbondale   (United States)

^b The Pennsylvania State University   (United States)

^c UNIVERSITY OF EXETER   (United Kingdom)

^d BEM Systems Inc   (United States)

^e Old Dominion University   (United States)

^f FLORIDA STATE UNIVERSITY   (United States)

^g UNIVERSITY OF TEHRAN   (Iran)

^h University of Illinois at Urbana Champaign   (United States)

ⁱ TECHNION ISRAEL INSTITUTE OF TECHNOLOGY   (Israel)

^j INTERA Incorporated   (United States)

^k TEXAS A AND M UNIVERSITY   (United States)

more..

Author keywords

Evolutionary algorithm; Evolutionary computation; Genetic algorithm

Indexed keywords

CALCULATIONS; GENETIC ALGORITHMS; GROUNDWATER; PLANNING; WASTEWATER; WASTEWATER TREATMENT; WATER DISTRIBUTION SYSTEMS; WATER RESOURCES EXPLORATION; WATER SUPPLY;

CROSS FERTILIZATION; EVOLUTIONARY COMPUTATION; EVOLUTIONARY COMPUTATIONS; FLUVIAL MODELING; GROUNDWATER SYSTEM; PARAMETER IDENTIFICATION; PROBLEM COMPLEXITY; SEWER SYSTEM; STATE OF THE ART; STATE-OF-THE-ART METHODS; SUB-DISCIPLINES; URBAN DRAINAGE; WATER RESOURCES PLANNING;

WATER RESOURCES;

ARTIFICIAL INTELLIGENCE; GENETIC ALGORITHM; OPTIMIZATION; PLANNING METHOD; UNCERTAINTY ANALYSIS; WATER MANAGEMENT; WATER PLANNING; WATER RESOURCE; WATER SUPPLY;

EID: 77955682503     PISSN: 07339496     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)WR.1943-5452.0000053     Document Type: Article

References (282)

1. Afshar M.H. Afshar A. Mariño M.A. Darbandi A.A. S. Hydrograph-based storm sewer design optimisation by genetic algorithm. Can. J. Civ. Eng. 2006, 33(3):319-325. CJCEB8, 0315-1468, 10.1139/L05-121,
2. Aksoy A. Culver T.B. Effect of sorption assumptions on aquifer remediation designs. Ground Water 2000, 38(2):200-208. GRWAAP, 0017-467X, 10.1111/j.1745-6584.2000.tb00331.x,
3. Aksoy A. Culver T.B. Impacts of physical and chemical heterogeneities on aquifer remediation design. J. Water Resour. Plann. Manage. 2004, 130(4):311-320. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:4(311),
4. Alperovits E. Shamir U. Design of optimal water distribution systems. Water Resour. Res. 1977, 13(6):885-900. WRERAQ, 0043-1397, 10.1029/WR013i006p00885,
5. Aly A. Peralta R.C. Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems. Water Resour. Res. 1999a, 35(8):2415-2425. WRERAQ, 0043-1397, 10.1029/1998WR900128,
6. Aly A. Peralta R.C. Optimal design of aquifer cleanup systems under uncertainty using a neural network and genetic algorithm. Water Resour. Res. 1999b, 35(8):2523-2532. WRERAQ, 0043-1397, 10.1029/98WR02368,
7. Amaziane B. Naji A. Ouazar D. Cheng A.H. D. Chance-constrained optimization of pumping in coastal aquifers by stochastic boundary element method and genetic algorithm. Comput., Mater., Continua 2005, 2(2):85-96. ZZZZZZ, 1546-2218,
8. Atkinson R. van Zyl J.E. Walters G.A. Savic D.A. Genetic algorithm optimisation of level-controlled pumping station operation. Water network modelling for optimal design and management 2000, 79-90. , and Centre for Water Systems, Exeter, U.K.
9. Babbar M. Minsker B.S. A multiscale master-slave parallel genetic algorithm with application to groundwater remediation design. Proc., Late Breaking Papers of the Proc. of the Genetic and Evolutionary Computation Conf. (GECCO 2002) 2002, and Morgan Kaufmann, New York.
10. Babbar M. Minsker B.S. Groundwater remediation design using multiscale genetic algorithms. J. Water Resour. Plann. Manage. 2006, 132(5):341-350. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2006)132:5(341),
11. Babovic V. Wu Z. Larsen L.C. Calibrating hydrodynamic models by means of simulated evolution. Proc., 1st Int. Conf. on Hydroinformatics 1994, 193-200. and Balkema, Rotterdam, The Netherlands.
12. Back T. Fogel D. Michalewicz Z. Handbook of evolutionary computation 2000, and IOP Publishing Ltd. and Oxford University Press, Bristol, U.K.
13. Barreto W.J. Vojinovic Z. Price R.K. Solomatine D.P. Approaches to multiobjective multi-tier optimisation in urban drainage planning. Proc., 7th Hydroinformatics Conf. 2006, , and Research Publishing, Chennai, India.
14. Bates B.C. Calibration of the SFB model using a simulated annealing approach. Proc., Water Down Under 94: Surface Hydrology and Water Resources Papers 1994, 1-6. Institute of Engineers, Barton, ACT, Australia.
15. Baú D. Mayer A. Stochastic management of pump-and-treat strategies using surrogate functions. Adv. Water Resour. 2006, 29(12):1901-1917. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.01.008,
16. Bayer P. Burger C.M. Finkel M. Computationally efficient stochastic optimization using multiple realizations. Adv. Water Resour. 2008, 31(2):399-417. AWREDI, 0309-1708, 10.1016/j.advwatres.2007.09.004,
17. Bayer P. Finkel M. Evolutionary algorithms for the optimization of advective control of contaminated aquifer zones. Water Resour. Res. 2004, 40:W06506. WRERAQ, 0043-1397, 10.1029/2003WR002675,
18. Bayer P. Finkel M. Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions. Water Resour. Res. 2007, 43:W02410. WRERAQ, 0043-1397, 10.1029/2005WR004753,
19. Behzadian K. Kapelan Z. Savic D.A. Ardeshir A. Stochastic sampling design using multiobjective genetic algorithm and adaptive neural networks. Environ. Modell. Software 2009, 24(4):530-541. EMSOFT, 1364-8152, 10.1016/j.envsoft.2008.09.013,
20. Bekele E.G. Nicklow J.W. Multiobjective management of ecosystem services by integrative watershed modeling and evolutionary algorithms. Water Resour. Res. 2005, 41:W10406. WRERAQ, 0043-1397, 10.1029/2005WR004090,
21. Beven K.J. Binley A. The future of distributed models: Model calibration and uncertainty prediction. Hydrolog. Process. 1992, 6:279-298. HYPRE3, 0885-6087, 10.1002/hyp.3360060305,
22. Bobbin J. Recknagel F. Inducing explanatory rules for the prediction of algal blooms by genetic algorithms. Environ. Int. 2001, 27:237-242. ENVIDV, 0160-4120, 10.1016/S0160-4120(01)00095-2,
23. Brill E.D. The use of optimization models in public-sector planning. Manage. Sci. 1979, 25:413-422. MSCIAM, 0025-1909, 10.1287/mnsc.25.5.413.
24. Broad D. Dandy G. Maier H. Water distribution system optimization using metamodels. J. Water Resour. Plann. Manage. 2005, 131(3):172-180. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:3(172),
25. Bürger C.M. Bayer P. Finkel M. Algorithmic funnel-and-gate system design optimization. Water Resour. Res. 2007, 43:W08426. WRERAQ, 0043-1397, 10.1029/2006WR005058,
26. Burn D.H. Yulianti J.S. Waste-load allocation using genetic algorithms. J. Water Resour. Plann. Manage. 2001, 127(2):121-129. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2001)127:2(121),
27. Bush C.A. Uber J.G. Sampling design methods for water distribution model calibration. J. Water Resour. Plann. Manage. 1998, 124(6):334-344. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1998)124:6(334),
28. Cai X. McKinney D.C. Lasdon L. Solving nonlinear water management models using a combined genetic algorithm and linear programming approach. Adv. Water Resour. 2001, 24:667-676. AWREDI, 0309-1708, 10.1016/S0309-1708(00)00069-5,
29. Cantu-Paz E. Efficient and accurate parallel genetic algorithms 2000, Kluwer, Norwell, Mass.
30. Cembrowicz R.G. Evolution strategies and genetic algorithms in water supply and waste water systems design. Proc., Water Resources and Distribution 1994, 27-39. W. R. Blain, eds., Comp. Mechanics, Southampton, U.K.
31. Cembrowicz R.G. Krauter G.E. Design of cost optimal sewer networks. Proc., 4th Int. Conf. on Urban Storm Drainage 1987, 367-372. W. Gujer, eds., Ecole Poly Fed., Lausanne, Switzerland.
32. Chadalavada S. Datta B. Dynamic optimal monitoring network design for transient transport of pollutants in groundwater aquifers. Water Resour. Manage. 2008, 22(6):651-670. WRMAEJ, 0920-4741, 10.1007/s11269-007-9184-x,
33. Chan Hilton A.B. Culver T.B. Constraint handling for genetic algorithms in optimal remediation design. J. Water Resour. Plann. Manage. 2000, 126(3):128-137. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2000)126:3(128),
34. Chan Hilton A.B. Culver T.B. Sensitivity of optimal groundwater remediation designs to residual water quality violations. J. Water Resour. Plann. Manage. 2001, 127(5):316-323. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2001)127:5(316),
35. Chan Hilton A.B. Culver T.B. Groundwater remediation design under uncertainty using genetic algorithms. J. Water Resour. Plann. Manage. 2005, 131(1):25-34. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:1(25),
36. Chang L.C. Chu H.J. Hsiao C.T. Optimal planning of a dynamic pump-treat-inject groundwater remediation system. J. Hydrol. 2007, 342(3-4):295-304. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.05.030,
37. Chang N.B. Chen W.C. Shieh W.K. Optimal control of wastewater treatment plants via integrated neural network and genetic algorithms. Civ. Eng. Environ. Syst. 2001, 18(1):1-17. ZZZZZZ, 1028-6628, 10.1080/02630250108970290,
38. Chen H.W. Chang N.B. Water pollution control in the river basin by genetic algorithm-based fuzzy multi-objective programming modeling. Water Sci. Technol. 1998, 37(8):55-63. WSTED4, 0273-1223, 10.1016/S0273-1223(98)00258-3,
39. Chen W.C. Chang N.B. Chen J.C. Rough set-based hybrid fuzzy-neural controller design for industrial wastewater treatment. Water Res. 2003, 37(1):95-107. WATRAG, 0043-1354, 10.1016/S0043-1354(02)00255-5,
40. Cho J.H. Sung K.S. Ha S.R. A river water quality management model for optimising regional wastewater treatment using a genetic algorithm. J. Environ. Manage. 2004, 73(3):229-242. JEVMAW, 0301-4797, 10.1016/j.jenvman.2004.07.004,
41. Cieniawski S.E. Eheart J.W. Ranjithan S.R. Using genetic algorithms to solve a multiobjective groundwater monitoring problem. Water Resour. Res. 1995, 31(2):399-409. WRERAQ, 0043-1397, 10.1029/94WR02039,
42. Coello Coello C. Lamont G.B. Van Veldhuizen D.A. Evolutionary algorithms for solving multi-objective problems 2007, and 2nd Ed., Springer, New York.
43. Cooper V.A. Nguyen V.T. V. Nicell J.A. Evaluation of global optimization methods for conceptual rainfall-runoff model calibration. Water Sci. Technol. 1997, 36(5):53-60. WSTED4, 0273-1223, 10.1016/S0273-1223(97)00461-7,
44. Cui L. Kuczera G. Optimizing urban water supply headworks using probabilistic search methods. J. Water Resour. Plann. Manage. 2003, 129(5):380-387. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2003)129:5(380),
45. Cui L. Kuczera G. Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resour. Res. 2005, 41:W05016. WRERAQ, 0043-1397, 10.1029/2004WR003517,
46. Cunha M.D. Groundwater cleanup: The optimization perspective (a literature review). Eng. Optimiz. 2002, 34(6):689-702. EGOPAX, 0305-215X, 10.1080/03052150215715.
47. Dandy G.C. Engelhardt M. Optimal scheduling of water pipe replacement using genetic algorithms. J. Water Resour. Plann. Manage. 2001, 127(4):214-223. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2001)127:4(214),
48. Dandy G.C. Engelhardt M. Multi-objective trade-offs between cost and reliability in the replacement of water mains. J. Water Resour. Plann. Manage. 2006, 132(2):79-88. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2006)132:2(79),
49. Das I. Dennis J.E. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct. Optim. 1997, 14(1):63-69. SOPTEQ, 0934-4373, 10.1007/BF01197559,
50. Deb K. Multi-objective optimization using evolutionary algorithms 2001, Wiley, New York.
51. Deb K. Agrawal R.B. Simulated binary crossover for continuous search space. Complex Syst. 1995, 9:115-148. CPSYEN, 0891-2513,
52. Deb K. Agrawal S. Pratap A. Meyarivan T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel problem solving from nature 2000, VI:849-858. 10.1007/3-540-45356-3_83, , and Vol. (PPSN-VI), Springer, Berlin/Heidelberg.
53. Deb K. Anand A. Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 2002, 10(4):371-395. ZZZZZZ, 1063-6560, 10.1162/106365602760972767,
54. Deb K. Mohan M. Mishra S. A fast multi-objective evolutionary algorithm for finding well-spread Pareto-optimal solutions. 2003, KanGAL Rep. No.2003002, Indian Institute of Technology, Kanpur, India.
55. Dessalegne T. Nicklow J.W. Minder E. Evolutionary computation to control unnatural water level fluctuations in multi-reservoir river systems. River. Res. Appl. 2004, 20(6):619-634. RRAIAQ, 1535-1459, 10.1002/rra.793,
56. Dhar A. Datta B. Multiobjective design of dynamic monitoring networks for detection of groundwater pollution. J. Water Resour. Plann. Manage. 2007, 133(4):329-338. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2007)133:4(329),
57. di Pierro F. Khu S.T. Savic D.A. An investigation on preference ordering ranking scheme in multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 2007, 11(1):17-45. ITEVF5, 1089-778X, 10.1109/TEVC.2006.876362,
58. di Pierro F. Khu S.T. Savic D.A. Berardi L. Efficient multi-objective optimal design of water distribution networks on a budget of simulations using hybrid algorithms. Environ. Modell. Software 2009, 24:202-213. EMSOFT, 1364-8152, 10.1016/j.envsoft.2008.06.008,
59. Dougherty D.E. Marryott R.A. Optimal groundwater management 1. Simulated annealing. Water Resour. Res. 1991, 27(10):2493-2508. WRERAQ, 0043-1397, 10.1029/91WR01468,
60. Duan Q. Gupta H.V. Sorooshian S. Rousseau A.N. Turcotte R. Advances in calibration of watershed models 2003, and AGU, Washington, D.C.
61. Duan Q. Gupta V.K. Sorooshian S. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 1992, 28(4):1015-1031. WRERAQ, 0043-1397, 10.1029/91WR02985,
62. Eckhardt K. Fohrer N. Frede H.-.G. Automatic model calibration. Hydrolog. Process. 2005, 19(3):651-658. HYPRE3, 0885-6087, 10.1002/hyp.5613,
63. Efstratiadis A. Koutsoyiannis D. An evolutionary annealing-simplex algorithm for global optimization of water resource systems. Proc., 5th Int. Conf. on Hydroinformatics (Hydroinformatics 2002) 2002, and IWA Publishing, Colchester, U.K.
64. Engelhardt M. Savic D.A. Skipworth P. Cashman A. Saul A.J. Walters G.A. Whole life costing: Application to water distribution network. Water Sci. Technol.: Water Supply 2003, 3(1-2):87-93. WSTWBM, 1606-9749,
65. Erickson M. Mayer A. Horn J. The niched Pareto genetic algorithm 2 applied to the design of groundwater remediation systems. Proc., First Int. Conf. on Evolutionary Multi-Criterion Optimization 2001, 681-695. and Springer, Berlin.
66. Erickson M.A. Mayer A. Horn J. Multi-objective optimal design of groundwater remediation systems: Application of the niched Pareto genetic algorithm (NPGA). Adv. Water Resour. 2002, 25(1):51-65. AWREDI, 0309-1708, 10.1016/S0309-1708(01)00020-3,
67. Espinoza F. Minsker B. Development of the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA). Water Resour. Res. 2006a, 42:W08501. WRERAQ, 0043-1397, 10.1029/2005WR004221,
68. Espinoza F. Minsker B. Goldberg D.E. Adaptive hybrid genetic algorithm for groundwater remediation design. J. Water Resour. Plann. Manage. 2005, 131(1):14-24. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:1(14),
69. Espinoza F.P. Minsker B.S. Effects of local search algorithms on groundwater remediation optimization using a self adaptive hybrid genetic algorithm. J. Comput. Civ. Eng. 2006b, 20(6):420-430. JCCEE5, 0887-3801, 10.1061/(ASCE)0887-3801(2006)20:6(420),
70. Farina M. Amato P. On the optimal solution definition for many-criteria optimization problems. Proc., NAFIPS-FLINT Int. Conf. 2002 2002, 233-238. J. Keller, O. Nasraoui, and and, eds., IEEE Computer Society Press, Piscataway, N.J.
71. Farmani R. Savic D.A. Walters G.A. Evolutionary multi-objective optimization in water distribution network design. Eng. Optimiz. 2005a, 37(2):167-183. EGOPAX, 0305-215X, 10.1080/03052150512331303436,
72. Farmani R. Savic D.A. Walters G.A. A hybrid technique for optimisation of branched urban water systems. Proc., 7th Hydroinformatics Conf. 2006, 1:985-992. and Vol., Research Publishing, Chennai, India.
73. Farmani R. Walters G.A. Savic D.A. Trade-off between total cost and reliability for any town water distribution network. J. Water Resour. Plann. Manage. 2005b, 131(3):161-171. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:3(161),
74. Fleming P.J. Purshouse R.C. Lygoe R.J. Many-objective optimization: An engineering design perspective 2005, and Springer, Berlin.
75. Fogel L.J. Owens A.J. Walsh M.J. Artificial intelligence through simulated evolution 1966, and Wiley, New York.
76. Fonseca C.M. Fleming P.J. Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. Proc., 5th Int. Conf. on Genetic Algorithms 1993, 416-423. S. Forrest, and . ed., Morgan Kaufmann, San Francisco.
77. Franchini M. Use of a genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models. J. Hydrol. Sci. 1996, 41(1):21-39. JHSCDB, 0324-8372, 10.1080/02626669609491476.
78. Franchini M. Galeati G. Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models. J. Hydrol. Sci. 1997, 42(3):357-379. JHSCDB, 0324-8372, 10.1080/02626669709492034,
79. Franchini M. Galeati G. Berra S. Global optimization techniques for the calibration of conceptual rainfall-runoff models. J. Hydrol. Sci. 1998, 43(3):443-458. JHSCDB, 0324-8372, 10.1080/02626669809492137,
80. Fujiwara O. Khang D.B. A two-phase decomposition method for optimal design of looped water distribution networks. Water Resour. Res. 1990, 26(4):539-549. WRERAQ, 0043-1397,
81. Gamerman D. Markov chain Monte Carlo: Statistical simulation for Bayesian inference 1997, Chapman & Hall, London.
82. Ganji A. Karamouz M. Khalili D. Development of stochastic conflict resolution models for reservoir operation. II. The value of players' information availability and cooperative behavior. Adv. Water Resour. 2007, 30:528-542. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.04.004,
83. Geiringer H. On the probability theory of linkage in Mendelian heredity. Ann. Math. Stat. 1944, 15:25-57. AASTAD, 0003-4851, 10.1214/aoms/1177731313.
84. Gessler J. Pipe network optimization by enumeration. Proc., Computer Applications for Water Resources 1985, 572-581. ASCE, New York.
85. Gibbs M.S. Dandy G.C. Maier H.R. A genetic algorithm calibration method based on convergence due to genetic drift. Inf. Sci. (N.Y.) 2008, 178(14):2857-2869. ISIJBC, 0020-0255, 10.1016/j.ins.2008.03.012,
86. Goldberg D.E. Genetic algorithms in search, optimization and machine learning 1989, Addison-Wesley, Reading, Mass.
87. Goldberg D.E. The design of innovation: Lessons from and for competent genetic algorithms 2002, Kluwer, Norwell, Mass.
88. Goldberg D.E. Deb K. Kargupta H. Harik G. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. 1993, IlliGAL Rep. No.93004, Illinois Genetic Algorithms Laboratory, Univ. of Illinois at Urbana-Champaign, Urbana, Ill.
89. Goldberg D.E. Kuo C.H. Genetic algorithms in pipeline optimization. J. Comput. Civ. Eng. 1987, 1(2):128-141. JCCEE5, 0887-3801, 10.1061/(ASCE)0887-3801(1987)1:2(128),
90. Guan J. Aral M.M. Optimal remediation with well locations and pumping rates selected as continuous decision variables. J. Hydrol. 1999, 221(1-2):20-42. JHYDA7, 0022-1694, 10.1016/S0022-1694(99)00079-7,
91. Guan J.B. Aral M.M. Optimal design of groundwater remediation systems using fuzzy set theory. Water Resour. Res. 2004, 40:W01518. WRERAQ, 0043-1397, 10.1029/2003WR002121,
92. Guan J.B. Aral M.M. Remediation system design with multiple uncertain parameters using fuzzy sets and genetic algorithm. J. Hydrol. Eng. 2005, 10(5):386-394. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2005)10:5(386),
93. Gumrah F. Durgut I. Oz B. Yeten B. The use of genetic algorithms for determining the transport parameters of core experiments. In Situ 2000a, 24(1):21-56. ISOMDJ, 0146-2520,
94. Gumrah F. Erbas D. Oz B. Altintas S. Genetic algorithms for optimizing the remediation of contaminated aquifer. Transp. Porous Media 2000b, 41(2):149-171. TPMEEI, 0169-3913, 10.1023/A:1006774101991,
95. Guo Y. Walters G.A. Khu S.T. Keedwell E.C. Optimal design of sewer networks using hybrid cellular automata and genetic algorithm. Proc., 5th IWA WorldWater Congress 2006, , and IWA Pub., London.
96. Guo Y. Walters G.A. Savic D.A. Optimal design of storm sewer networks: Past, present and future. Proc., 11th Int. Conf. on Urban Drainage (ICUD 2008) 2008, 10. (CD-ROM), IWA Pub., London.
97. Gupta H.V. Bastidas L.A. Sorroshian S. Shuttleworth W.J. Yang Z.L. Parameter estimation of a land surface scheme using multicriteria methods. J. Geophys. Res. 1999, 104(D16):19491-19503. JGREA2, 0148-0227, 10.1029/1999JD900154,
98. Guria C. Bhattacharya P.K. Gupta S.K. Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA). Comput. Chem. Eng. 2005, 29(9):1977-1995. CCENDW, 0098-1354, 10.1016/j.compchemeng.2005.05.002,
99. Halhal D. Walters G.A. Ouazar D. Savic D.A. Multi-objective improvement of water distribution systems using a structured messy genetic algorithm approach. J. Water Resour. Plann. Manage. 1997, 123(3):137-146. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1997)123:3(137),
100. Halhal D. Walters G.A. Savic D.A. Ouazar D. Scheduling of water distribution system rehabilitation using structured messy genetic algorithms. Evol. Comput. 1999, 7(3):311-329. ZZZZZZ, 1063-6560, 10.1162/evco.1999.7.3.311,
101. Hansen N. Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 2001, 9(2):159-195. ZZZZZZ, 1063-6560, 10.1162/106365601750190398,
102. Hansen N.S. Muller D. Koumoutsakos P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 2003, 11(1):1-18. ZZZZZZ, 1063-6560, 10.1162/106365603321828970,
103. Harrell L.J. Ranjithan S. Integrated detention pond design and land use planning for watershed management. J. Water Resour. Plann. Manage. 2003, 129(2):98-106. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2003)129:2(98),
104. He K.J. Zheng L. Dong S.B. Tang L.Q. Wu J.F. Zheng C.M. PGO: A parallel computing platform for global optimization based on genetic algorithm. Comput. Geosci. 2007, 33(3):357-366. CGEODT, 0098-3004, 10.1016/j.cageo.2006.09.002,
105. He L. Huang G.H. Lu H.W. Zeng G.M. Optimization of surfactant-enhanced aquifer remediation for a laboratory BTEX system under parameter uncertainty. Environ. Sci. Technol. 2008, 42(6):2009-2014. ESTHAG, 0013-936X, 10.1021/es071106y,
106. Holland J.H. Outline for a logical theory of adaptive systems. J. Assoc. Comput. Mach. 1962, 3:297-314. JACOAH, 0004-5411.
107. Holland J.H. Adaptation in natural and artificial systems 1975, University of Michigan Press, Ann Arbor, Mich.
108. Hsiao C.T. Chang L.C. Optimizing remediation of an unconfined aquifer using a hybrid algorithm. Ground Water 2005, 43(6):904-915. GRWAAP, 0017-467X,
109. Hu Z.Y. Chan C.W. Huang G.H. Multi-objective optimization for process control of the in-situ bioremediation system under uncertainty. Eng. Applic. Artif. Intell. 2007, 20(2):225-237. EAAIE6, 0952-1976, 10.1016/j.engappai.2006.06.008,
110. Huang C.L. Mayer A.S. Pump-and-treat optimization using well locations and pumping rates as decision variables. Water Resour. Res. 1997, 33(5):1001-1012. WRERAQ, 0043-1397, 10.1029/97WR00366,
111. Huang W. Yuan L. Lee C. Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multireservoir system. Water Resour. Res. 2002, 38:319-333. WRERAQ, 0043-1397, 10.1029/2001WR001122,
112. Ines A.V. M. Droogers P. Inverse modeling in estimating soil hydraulic functions: A genetic algorithm approach. Hydrology Earth Syst. Sci. 2002, 6(1):49-65. ZZZZZZ, 1027-5606,
113. Jin Y. Olhofer M. Sendhoff B. A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 2002, 6(5):481-494. ITEVF5, 1089-778X, 10.1109/TEVC.2002.800884,
114. Jonkergouw P. Khu S.-.T. Kapelan Z. Savic D.A. Water quality model calibration under unknown demands. J. Water Resour. Plann. Manage. 2008, 134(4):326-336. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2008)134:4(326),
115. Jourdan L. Corne D.W. Savic D.A. Walters G.A. LEMMO: Hybridising rule induction and NSGA II for multi-objective water systems design. Proc., 8th Int. Conf. on Computing and Control for the Water Industry 2006, 2:45-50. D.A. Savic, G.A. Walters, R. King, S.-T. Khu, , and Vol. eds., Exeter Press, Exeter, U.K.
116. Kalwij I.M. Peralta R.C. Simulation/optimization modeling for robust pumping strategy design. Ground Water 2006, 44(4):574-582. GRWAAP, 0017-467X, 10.1111/j.1745-6584.2006.00176.x,
117. Kapelan Z. Savic D.A. Walters G.A. A hybrid inverse transient model for leakage detection and roughness calibration in pipe networks. J. Hydraul. Res. 2003a, 41(5):481-492. JHYRAF, 0022-1686,
118. Kapelan Z. Savic D.A. Walters G.A. Multiobjective sampling design for water distribution model calibration. J. Water Resour. Plann. Manage. 2003b, 129(6):466-479. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2003)129:6(466),
119. Kapelan Z. Savic D.A. Walters G.A. Multiobjective design of water distribution systems under uncertainty. Water Resour. Res. 2005, 41:W11407. WRERAQ, 0043-1397, 10.1029/2004WR003787,
120. Kapelan Z. Savic D.A. Walters G.A. Calibration of WDS hydraulic models using the Bayesian recursive procedure. J. Hydraul. Eng. 2007, 133(8):927-936. JHEND8, 0733-9429, 10.1061/(ASCE)0733-9429(2007)133:8(927),
121. Karamouz M. Mojahedi A. Ahmadi A. Economic assessment of operational policies of inter-basin water transfer. Water Resour. Res. 2007, 3(2):86-101. WRERAQ, 0043-1397, 10.1029/2000WR900411, and (in Persian).
122. Karpouzos D.K. Delay F. Katsifarakis K.L. de Marsily G. A multipopulation genetic algorithm to solve the inverse problem in hydrogeology. Water Resour. Res. 2001, 37(9):2291-2302. WRERAQ, 0043-1397,
123. Katsifarakis K.L. Karpouzos D.K. Theodossiou N. Combined use of BEM and genetic algorithms in groundwater flow and mass transport problems. Eng. Anal. Boundary Elem. 1999, 23(7):555-565. EABAEL, 0955-7997, 10.1016/S0955-7997(99)00011-9,
124. Keedwell E. Khu S.T. Using cellular automata to seed genetic algorithms for water distribution network design problems. Eng. Applic. Artif. Intell. 2005, 18(4):461-472. EAAIE6, 0952-1976, 10.1016/j.engappai.2004.10.001.
125. Kerachian R. Karamouz M. Waste-load allocation for seasonal river water quality management: Application of sequential dynamic genetic algorithms. J. of Scientia Iranica 2005, 12(2):117-130.
126. Kerachian R. Karamouz M. Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach. Water Resour. Res. 2006, 42:W12401. WRERAQ, 0043-1397, 10.1029/2005WR004575,
127. Kerachian R. Karamouz M. A stochastic conflict resolution model for water quality management in reservoir-river system. Adv. Water Resour. 2007, 30(4):866-882. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.07.005.
128. Kerachian R. Karamouz M. Soltay F. Optimal reservoir operation considering the water quality issues: Application of adaptive neuro-fuzzy inference systems (ANFIS). ASCE World Environmental and Water Resources Congress 2006 2006, and Omaha, Neb.
129. Khu S.-.T. Madsen H. Multiobjective calibration with Pareto preference ordering: An application to rainfall-runoff model calibration. Water Resour. Res. 2005, 41:W03004. WRERAQ, 0043-1397, 10.1029/2004WR003041,
130. Kirkpatrick S. Gelatt C.D. Vecchi M.P. Optimization by simulated annealing. Science 1983, 220(4598):671-680. SCIEAS, 0036-8075, 10.1126/science.220.4598.671.
131. Ko N.Y. Lee K.K. Hyun Y. Optimal groundwater remediation design of a pump and treat system considering cleanup time. Geosci. J. 2005, 9(1):23-31. ZZZZZZ, 1598-7477, 10.1007/BF02910551.
132. Kobayashi K. Hinkelmann R. Helmig R. Development of a simulation-optimization model for multiphase systems in the subsurface: A challenge to real-world simulation-optimization. J. Hydroinform. 2008, 10(2):139-152. ZZZZZZ, 1464-7141, 10.2166/hydro.2008.013.
133. Kollat J.B. Reed P. A framework for visually interactive decision-making and design using evolutionary multiobjective optimization (VIDEO). Environ. Modell. Software 2007a, 22(12):1691-1704. EMSOFT, 1364-8152, 10.1016/j.envsoft.2007.02.001.
134. Kollat J.B. Reed P.M. Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Adv. Water Resour. 2006, 29(6):792-807. AWREDI, 0309-1708, 10.1016/j.advwatres.2005.07.010.
135. Kollat J.B. Reed P.M. A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications. Adv. Water Resour. 2007b, 30(3):408-419. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.05.009.
136. Kollat J.B. Reed P.M. Kasprzy J.R. A new epsilon-dominance hierarchical Bayesian optimization algorithm for large multiobjective monitoring network design problems. Adv. Water Resour. 2008, 31:828-845. AWREDI, 0309-1708, 10.1016/j.advwatres.2008.01.017,
137. Koza J.R. Genetic programming 1992, MIT Press, Cambridge, Mass.
138. Krishnakumar K. Micro-genetic algorithms for stationary and non-stationary function optimization. Proc., SPIE: Intelligent Control and Adaptive Systems 1989, 1196:289-296. Vol., SPIE, Bellingham, Wash.
139. Kuczera G. Efficient subspace probabilistic parameter optimization for catchment models. Water Resour. Res. 1997, 33(1):177-185. WRERAQ, 0043-1397, 10.1029/96WR02671.
140. Kumar S.V. Ranjithan S. Evaluation of the constraint method-based multiobjective evolutionary algorithm (CMEA) for a three-objective optimization problem. Proc., Genetic and Evolutionary Computation Conf., GECCO 2002 2002, 431-438. W. B. Langdon, eds., Morgan Kaufmann, New York.
141. Kuo J.T. Wang Y.Y. Lung W. A hybrid neural-genetic algorithm for reservoir water quality management. Water Res. 2006, 40:1367-1376. WATRAG, 0043-1354, 10.1016/j.watres.2006.01.046,
142. Labadie J. Optimal operation of multireservoir systems: State-of-the-art review. J. Water Resour. Plann. Manage. 2004, 130(2):93-111. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:2(93).
143. Langeveld J.G. Clemens F.H. L. R. van der Graaf J.H. J. M. Increasing wastewater system performance-The importance of interactions between sewerage and wastewater treatment. Water Sci. Technol. 2002, 45(3):45-52. WSTED4, 0273-1223.
144. Laumanns M. Thiele L. Zitzler E. Deb K. Archiving with guaranteed convergence and diversity in multi-objective optimization. Proc., Gecco-2002 Genetic and Evolutionary Computation Conf. 2002, 447-439. W. B. Langdon, eds. Morgan Kaufmann, New York.
145. Lavric V. Iancu P. Plesu V. Genetic algorithm optimization of water consumption and wastewater network topology. J. Cleaner Prod. 2005, 13(15):1405-1415. ZZZZZZ, 0959-6526, 10.1016/j.jclepro.2005.04.014.
146. Lee Y.M. Ellis J.H. Comparison of algorithms for nonlinear integer optimization: Application to monitoring network design. J. Environ. Eng. 1996, 122(6):524-531. JOEEDU, 0733-9372, 10.1061/(ASCE)0733-9372(1996)122:6(524).
147. Li Y. Du J. Yao P.J. Design of water network with multiple contaminants and zero discharge. Chin. J. Chem. Eng. 2003, 11(5):559-564. CJCEEB, 1004-9541.
148. Liang L.Y. Thompson R.G. Young D.M. Optimising the design of sewer networks using genetic algorithms and tabu search. Eng., Constr., Archit. Manage. 2004, 11(2):101-112. ZZZZZZ, 0969-9988, 10.1108/09699980410527849.
149. Lingireddy S. Ormsbee L.E. Optimal network calibration model based on genetic algorithms. 1998, Tech. Rep., Univ. of Kentucky, Lexington, Ky.
150. Lingireddy S. Ormsbee L.E. Optimal network calibration model based on genetic algorithms. WRPMD 1999 1999, 102:45. E. M. Wilson, Vol., ed., ASCE, Tempe.
151. Liong S.Y. Khu S.T. Chan W.T. Derivation of Pareto front with genetic algorithm and neural network. J. Hydrol. Eng. 2001, 6(1):52-61. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2001)6:1(52).
152. Liu W.H. Medina M.A. Thomann W. Piver W.T. Jacobs T.L. Optimization of intermittent pumping schedules for aquifer remediation using a genetic algorithm. J. Am. Water Resour. Assoc. 2000, 36(6):1335-1348. JWRAF5, 1093-474X, 10.1111/j.1752-1688.2000.tb05730.x.
153. Loughlin D.H. Ranjithan S.R. Baugh J.W. Brill E.D. Application of GAs for the design of ozone control strategies. J. Air Waste Manage. Assoc. 2000, 50:1050-1063. JAWAFC, 1096-2247,
154. Mackle G. Savic D.A. Walters G.A. Application of genetic algorithms to pump scheduling for water supply. Proc., Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA '95 1995, 400-405. IEE, London,
155. Madsen H. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J. Hydrol. 2000, 235:276-288. JHYDA7, 0022-1694, 10.1016/S0022-1694(00)00279-1.
156. Madsen H. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Adv. Water Resour. 2003, 26(2):205-216. AWREDI, 0309-1708, 10.1016/S0309-1708(02)00092-1,
157. Mahinthakumar G. Sayeed M. Hybrid genetic algorithm: Local search
158. Mantoglou A. Kourakos G. Optimal groundwater remediation under uncertainty using multi-objective optimization. Water Resour. Manage. 2007, 21(5):835-847. WRMAEJ, 0920-4741, 10.1007/s11269-006-9109-0.
159. Maskey S. Jonoski A. Solomatine D.P. Groundwater remediation strategy using global optimization algorithms. J. Water Resour. Plann. Manage. 2002, 128(6):431-440. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2002)128:6(431).
160. Matott L.S. Rabideau A.J. Craig J.R. Pump-and-treat optimization using analytic element method flow models. Adv. Water Resour. 2006, 29(5):760-775. AWREDI, 0309-1708, 10.1016/j.advwatres.2005.07.009.
161. Mayer A.S. Kelley C.T. Miller C.T. Optimal design for problems involving flow and transport phenomena in saturated subsurface systems. Adv. Water Resour. 2002, 25(8-12):1233-1256. AWREDI, 0309-1708, 10.1016/S0309-1708(02)00054-4.
162. McKinney D.C. Lin M.D. Genetic algorithm solution of groundwater management models. Water Resour. Res. 1994, 30(6):1897-1906. WRERAQ, 0043-1397, 10.1029/94WR00554.
163. McLaughlin D. Townley L.R. A reassessment of the groundwater inverse problem. Water Resour. Res. 1996, 32(5):1131-1161. WRERAQ, 0043-1397, 10.1029/96WR00160.
164. McPhee J. Yeh W.G. Multiobjective optimization for sustainable groundwater management in semiarid regions. J. Water Resour. Plann. Manage. 2004, 130(6):490-497. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:6(490).
165. Merabtene T. Kawamra A. Jinno K. Olsson J. Risk assessment for optimal drought management of an integrated water resources system using a genetic algorithm. Hydrolog. Process. 2002, 16(11):2189-2208. HYPRE3, 0885-6087, 10.1002/hyp.1150,
166. Michalski R. Learnable evolution model: Evolutionary processes guided by machine learning. Mach. Learn. 2000, 38(1-2):9-40. ZZZZZZ, 0885-6125, 10.1023/A:1007677805582.
167. Miller B.L. Goldberg D.E. Optimal sampling for genetic algorithms. Proc., Intelligent Engineering Systems through Artificial Neural Networks (ANNIE '96) 1996, 6:291-298. C. H. Dagli, M. Akay, C. L. P. Chan, B. R. Fernandez, J. Ghosh, eds. Vol., ASME Press, New York.
168. Milly P.C.D. Stationarity is dead: Whither water management?. Science 2008, 319(5863):573-574. SCIEAS, 0036-8075, 10.1126/science.1151915,
169. Mugunthan P. Shoemaker C. Comparison of function approximation, heuristic, and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models. Water Resour. Res. 2005, 41:W1427. WRERAQ, 0043-1397, 10.1029/2005WR004134,
170. Muleta M.K. Nicklow J.W. Application of artificial neural networks and evolutionary algorithms to watershed management. Water Resour. Manage. 2004, 18(5):459-482. WRMAEJ, 0920-4741, 10.1023/B:WARM.0000049140.64059.d1.
171. Muleta M.K. Nicklow J.W. Decision support for watershed management using evolutionary algorithms. J. Water Resour. Plann. Manage. 2005, 131(1):35-44. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:1(35).
172. Munavalli G.R. Mohan-Kumar M.S. Optimal scheduling of multiple chlorine sources in water distribution systems. J. Water Resour. Plann. Manage. 2003, 129(6):493-504. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2003)129:6(493).
173. Murthy Z.V. P. Vengal J.C. Optimization of a reverse osmosis system using genetic algorithm. Sep. Sci. Technol. 2006, 41(4):647-663. SSTEDS, 0149-6395, 10.1080/01496390500526854.
174. Nagesh Kumar D. Srinivasa Raju K. Ashok B. Optimal reservoir operation for irrigation of multiple crops using genetic algorithms. J. Irrig. Drain. Eng. 2006, 132(2):123-129. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(2006)132:2(123).
175. Confronting the nation's water problems: The role of research 2004, National Research Council, Washington, D.C.
176. Ndiritu J.G. Daniell T.M. An improved genetic algorithm for continuous and mixed discrete-continuous optimization. Eng. Optimiz. 1999, 31:589-614. EGOPAX, 0305-215X, 10.1080/03052159908941388,
177. Nelder J.A. Mead R. A simplex method for function minimization. Comput. J. 1965, 7:308-313. CMPJA6, 0010-4620,
178. Nixon J. Dandy G.C. Simpson A.R. A genetic algorithm for optimizing off-farm irrigation scheduling. J. Hydroinform. 2001, 3(1):11-22. ZZZZZZ, 1464-7141.
179. Oliveira R. Loucks D.P. Operating rules for multireservoir systems. Water Resour. Res. 1997, 33(4):839-852. WRERAQ, 0043-1397, 10.1029/96WR03745.
180. Ostfeld A. Salomons E. Sensor network design proposal for the battle of the water sensor networks (BWSN). Proc., 8th Annual Int. Symp. on Water Distribution Systems Analysis 2006, (CD-ROM), ASCE, Reston, Va.
181. Pareto V. Cours D'Economie Politique 1896, Rouge, Lausanne, Switzerland.
182. Park D.K. Ko N.Y. Lee K.K. Optimal groundwater remediation design considering effects of natural attenuation processes: Pumping strategy with enhanced-natural-attenuation. Geosci. J. 2007, 11(4):377-385. ZZZZZZ, 1598-7477, 10.1007/BF02857053.
183. Parker M.A. Savic D.A. Walters G.A. Kapelan Z. SewerNet: A genetic algorithm application for optimising urban drainage systems. Proc., Int. Internet Conf. on Urban Drainage 2000, Hydroinform, Prague, Czech Rep.
184. Pelikan M. Bayesian optimization algorithm: From single level to hierarchy. 2002, IlliGAL Rep. No.2002023, Illinois Genetic Algorithms Laboratory, Univ. of Illinois at Urbana-Champaign, Urbana, Ill.
185. Perez-Pedini C. Limbrunner J.F. Vogel R.M. Optimal location of infiltration-based best management practices for storm water management. J. Water Resour. Plann. Manage. 2005, 131(6):441-448. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:6(441).
186. Prasad T.D. Park N.-.S. Multiobjective genetic algorithms for design of water distribution networks. J. Water Resour. Plann. Manage. 2004, 130(1):73-82. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:1(73).
187. Prasad T.D. Walters G.A. Savic D.A. Booster disinfection of water supply networks: A multi-objective approach. J. Water Resour. Plann. Manage. 2004, 130(5):367-376. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:5(367).
188. Price W.L. Global optimization by controlled random search. J. Optim. Theory Appl. 1983, 40:333-348. JOTABN, 0022-3239, 10.1007/BF00933504.
189. Qin X.S. Huang G.H. He L. Simulation and optimization technologies for petroleum waste management and remediation process control. J. Environ. Manage. 2009, 90(1):54-76. JEVMAW, 0301-4797, 10.1016/j.jenvman.2008.07.002.
190. Rao Z. Salomons E. Development of a real-time, near-optimal control process for water-distribution networks. J. Hydroinform. 2007, 9(1):25-37. ZZZZZZ, 1464-7141, 10.2166/hydro.2006.015.
191. Rauch W. Harremoes P. Genetic algorithms in real time control applied to minimize transient pollution from urban waste water systems. Water Res. 1999, 33(5):1265-1277. WATRAG, 0043-1354, 10.1016/S0043-1354(98)00304-2.
192. Rechenberg I. Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution 1973, Frommann-Holzboog, Stuttgart, Germany.
193. Reed P. Kollat J.B. Devireddy V.K. Using interactive archives in evolutionary multiobjective optimization: A case study for long-term groundwater monitoring design. Environ. Modell. Software 2007, 22(5):683-692. EMSOFT, 1364-8152, 10.1016/j.envsoft.2005.12.021.
194. Reed P. Minsker B.S. Striking the balance: Long-term groundwater monitoring design for conflicting objectives. J. Water Resour. Plann. Manage. 2004, 130(2):140-149. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:2(140).
195. Reed P. Minsker B.S. Goldberg D.E. Designing a competent simple genetic algorithm for search and optimization. Water Resour. Res. 2000a, 36(12):3757-3761. WRERAQ, 0043-1397, 10.1029/2000WR900231.
196. Reed P. Minsker B.S. Goldberg D.E. A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data. J. Hydroinform. 2001, 3(2):71-90. ZZZZZZ, 1464-7141.
197. Reed P. Minsker B.S. Goldberg D.E. Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm-II. Water Resour. Res. 2003, 39:21-25. WRERAQ, 0043-1397, 10.1029/2002WR001483,
198. Reed P. Minsker B.S. Valocchi A.J. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 2000b, 36(12):3731-3741. WRERAQ, 0043-1397, 10.1029/2000WR900232.
199. Reed P. Yamaguchi S. Making it easier to use the multiple population parallelization scheme for evolutionary algorithms. Proc., World Water and Environmental Resources Congress 2004a, ASCE, Reston, Va.
200. Reed P. Yamaguchi S. Simplifying the parameterization of real-coded evolutionary algorithms. Proc., World Water and Environmental Resources Congress 2004b, ASCE, Reston, Va.
201. Regis R.G. Shoemaker C.A. Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans. Evol. Comput. 2004, 8(5):490-505. ITEVF5, 1089-778X, 10.1109/TEVC.2004.835247.
202. Ren X. Minsker B.S. Which groundwater remediation objective is better: A realistic one or a simple one?. J. Water Resour. Plann. Manage. 2005, 131(5):351-361. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:5(351).
203. Ritzel B.J. Eheart J.W. Ranjithan S.R. Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resour. Res. 1994, 30(5):1589-1603. WRERAQ, 0043-1397, 10.1029/93WR03511.
204. Rizzo D.M. Dougherty D.E. Design optimization for multiple management period groundwater remediation. Water Resour. Res. 1996, 32(8):2549-2561. WRERAQ, 0043-1397, 10.1029/96WR01334.
205. Rogers L.L. Dowla F.U. Johnson V.M. Optimal field-scale groundwater remediation using neural networks and the genetic algorithm. Environ. Sci. Technol. 1995, 29(5):1145-1155. ESTHAG, 0013-936X, 10.1021/es00005a003.
206. Savic D. Walters G. Genetic algorithms for least cost design of water distribution networks. J. Water Resour. Plann. Manage. 1997, 123(2):67-77. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1997)123:2(67).
207. Savic D.A. Kapelan Z. Jonkergouw P.M. R. Quo vadis water distribution model calibration?. Urban Water 2009, 6(1):3-22. URWAFE, 1462-0758, 10.1080/15730620802613380,
208. Savic D.A. Walters G.A. An evolution program for optimal pressure regulation in water distribution networks. Eng. Optimiz. 1995a, 24(3):197-219. EGOPAX, 0305-215X, 10.1080/03052159508941190.
209. Savic D.A. Walters G.A. Genetic algorithm techniques for calibrating network models. 1995b, Tech. Rep.95/12, Centre for Systems and Control Engineering, Univ. of Exeter, Exeter, U.K.
210. Savic D.A. Walters G.A. Schwab M. Multiobjective genetic algorithms for pump scheduling in water supply. Proc., AISB '97, Lecture Notes in Computer Science 1305 1997, 227-236. D. Corne, J. L. Shapiro eds., Springer, Berlin.
211. Schaake J.C. Lai D. Linear programming and dynamic programming application to water distribution network design. 1969, Rep. No.116, Dept. of Civil Engineering, MIT, Cambridge, Mass.
212. Schutze M. Butler D. Beck M.B. Optimisation of control strategies for the urban wastewater system-An integrated approach. Water Sci. Technol. 1999, 39(9):209-216. WSTED4, 0273-1223, 10.1016/S0273-1223(99)00235-8.
213. Schwefel H.-.P. Numerical optimization of computer models 1981, Wiley, Chichester.
214. Schwefel H.-.P. Evolution and optimum seeking 1995, Wiley, New York.
215. Shieh H.J. Peralta R.C. Optimal in situ bioremediation design by hybrid genetic algorithm-simulated annealing. J. Water Resour. Plann. Manage. 2005, 131(1):67-78. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:1(67).
216. Sidiropoulos E. Tolikas P. Genetic algorithms and cellular automata in aquifer management. Appl. Math. Model. 2008, 32(4):617-640. AMMODL, 0307-904X, 10.1016/j.apm.2007.01.005.
217. Simpson A.R. Dandy G.C. Murphy L.J. Genetic algorithms compared to other techniques for pipe optimization. J. Water Resour. Plann. Manage. 1994, 120(4):423-443. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1994)120:4(423).
218. Singh A. Minsker B.S. Uncertainty-based multiobjective optimization of groundwater remediation design. Water Resour. Res. 2008, 44:W02404. WRERAQ, 0043-1397, 10.1029/2005WR004436,
219. Singh A. Minsker B.S. Valocchi A.J. An interactive multi-objective optimization framework for groundwater inverse modeling. Adv. Water Resour. 2008, 31:1269-1283. AWREDI, 0309-1708, 10.1016/j.advwatres.2008.05.005,
220. Sinha E. Minsker B.S. Multiscale island injection genetic algorithms for groundwater remediation. Adv. Water Resour. 2007, 30(9):1933-1942. AWREDI, 0309-1708, 10.1016/j.advwatres.2007.03.006.
221. Skaggs R.L. Mays L.W. Vail L.W. Application of enhanced annealing to groundwater remediation design. J. Am. Water Resour. Assoc. 2001, 37(4):867-875. JWRAF5, 1093-474X, 10.1111/j.1752-1688.2001.tb05518.x.
222. Smalley J.B. Minsker B.S. Goldberg D.E. Risk-based in situ bioremediation design using a noisy genetic algorithm. Water Resour. Res. 2000, 36(10):3043-3052. WRERAQ, 0043-1397, 10.1029/2000WR900191.
223. Solomatine D.P. Genetic and other global optimization algorithms comparison and use in calibration problems. Proc. Hydroinformatics '98 1998, 1021-1028. V. Babovic, L. C. Larsen, eds., Balkema, Rotterdam, The Netherlands,
224. Solomatine D.P. Dibike Y.B. Kukuric N. Automatic calibration of groundwater models using global optimization techniques. J. Hydrol. Sci. 1999, 44(6):879-894. JHSCDB, 0324-8372, 10.1080/02626669909492287.
225. Sorooshian S. Duan Q. Gupta V.K. Calibration of rainfall-runoff models: Application of global optimization to the Sacramento soil moisture accounting model. Water Resour. Res. 1993, 29(4):1185-1194. WRERAQ, 0043-1397, 10.1029/92WR02617.
226. Storn R. Price K. Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 1997, 11:341-359. JGOPEO, 0925-5001, 10.1023/A:1008202821328,
227. Suggala S.V. Bhattacharya P.K. Real coded genetic algorithm for optimization of pervaporation process parameters for removal of volatile organics from water. Ind. Eng. Chem. Res. 2003, 42(13):3118-3128. IECRED, 0888-5885, 10.1021/ie020183t.
228. Sumner N. Fleming P. Bates B. Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing. J. Hydrol. 1997, 197:166-188. JHYDA7, 0022-1694, 10.1016/S0022-1694(96)03277-5,
229. Sun M. Zheng C.M. Long-term groundwater management by a MODFLOW based dynamic optimization tool. J. Am. Water Resour. Assoc. 1999, 35(1):99-111. JWRAF5, 1093-474X, 10.1111/j.1752-1688.1999.tb05455.x.
230. Takagi H. Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 2001, 89(9):1275-1296. IEEPAD, 0018-9219, 10.1109/5.949485.
231. Tang K. Karney B. Pendlebury M. Zhang F. Inverse transient calibration of water distribution systems using genetic algorithms. Proc., Water Industry Systems: Modelling and Optimization Applications 1999, 1. D. A. Savic, G. A. Walters, Vol. eds., Research Studies Press, Baldock, U.K.
232. Tang Y. Reed P. Kollat J.B. Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications. Adv. Water Resour. 2007, 30(3):335-353. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.06.006.
233. Tang Y. Reed P. Wagener T. How efficient and effective are evolutionary multiobjective algorithms at hydrologic model calibration?. Hydrol. Earth Syst. Sci. 2006, 10:289-307. 1027-5606, 10.5194/hess-10-289-2006.
234. Thierens D. Goldberg D.E. Pereira A.G. Domino convergence, drift, and the temporal-salience structure of problems. Proc., IEEE Int. Conf. on Evolutionary Computation 1998, 535-540. IEEE, Piscataway, N.J.,
235. Thyer M. Kuczera G. Bates B.C. Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms. Water Resour. Res. 1999, 35(3):767-773. WRERAQ, 0043-1397, 10.1029/1998WR900058.
236. Tsai F.T. C. Sun N.Z. Yeh W.W. G. A combinatorial optimization scheme for parameter structure identification in groundwater modeling. Ground Water 2003, 41(2):156-169. GRWAAP, 0017-467X, 10.1111/j.1745-6584.2003.tb02579.x.
237. Tsai M.-.J. Chang C.-.T. Water usage and treatment network design using genetic algorithm. Ind. Eng. Chem. Res. 2001, 40:4874-4888. IECRED, 0888-5885, 10.1021/ie010011g,
238. Vairavamoorthy K. Ali M. Pipe index vector: A method to improve genetic-algorithm-based pipe optimization. J. Hydraul. Eng. 2005, 131(12):1117-1125. JHEND8, 0733-9429, 10.1061/(ASCE)0733-9429(2005)131:12(1117).
239. Vamvakeridou-Lyroudia L.S. Walters G.A. Savic D.A. Fuzzy multiobjective optimization of water distribution networks. J. Water Resour. Plann. Manage. 2005, 131(6):467-476. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:6(467).
240. van Zyl J. Savic D.A. Walters G.A. Operational optimization of water distribution systems using a hybrid genetic algorithm method. J. Water Resour. Plann. Manage. 2004, 130(2):160-170. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:2(160).
241. Vasquez J.A. Maier H.R. Lence B.J. Tolson B.A. Foschi R.O. Achieving water quality system reliability using genetic algorithms. J. Environ. Eng. 2000, 126(10):954-962. JOEEDU, 0733-9372, 10.1061/(ASCE)0733-9372(2000)126:10(954).
242. Vítkovský J.P. Liggett J.A. Simpson A.R. Lambert M.F. Optimal measurement site locations for inverse transient analysis in pipe networks. J. Water Resour. Plann. Manage. 2003, 129(6):480-492. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2003)129:6(480),
243. Vítkovský J.P. Simpson A.R. Calibration and leak detection in pipe networks using inverse transient analysis and genetic algorithms. 1997, Tech. Rep. No.R 157, Dept. of Civil and Environmental Engineering, Univ. of Adelaide, Adelaide, Australia.
244. Vítkovský J.P. Simpson A.R. Lambert M.F. Leak detection and calibration using transients and genetic algorithms. J. Water Resour. Plann. Manage. 2000, 126(4):262-265. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2000)126:4(262).
245. Vorosmarty C. Green P. Salisbury J. Lammers R. Global water resources: Vulnerability from climate change and population growth. Science 2000, 289:284-288. SCIEAS, 0036-8075, 10.1126/science.289.5477.284,
246. Vrugt J. Gupta H.V. Bastidas L.A. Bouten W. Sorooshian S. Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour. Res. 2003a, 39:1214. WRERAQ, 0043-1397, 10.1029/2002WR001746,
247. Vrugt J. Gupta H.V. Bouten W. Sorooshian S. A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour. Res. 2003b, 39:SWC1.1-SWC1.16. WRERAQ, 0043-1397, 10.1029/2002WR001642,
248. Vrugt J.A. Inverse modeling of large-scale spatially distributed vadose zone properties using global optimization. Water Resour. Res. 2004, 40:W06503. WRERAQ, 0043-1397, 10.1029/2003WR002706.
249. Vrugt J.A. Robinson B.A. Improved evolutionary search from genetically adaptive multi-method search. Proc. Natl. Acad. Sci. U.S.A. 2007, 104(3):708-711. PNASA6, 0027-8424, 10.1073/pnas.0610471104.
250. Wagener T. Gupta H.V. Model identification for hydrological forecasting under uncertainty. Stochastic Environ. Res. Risk Assess. 2005, 19(6):378-387. 10.1007/s00477-005-0006-5, 1436-3240.
251. Walters G.A. Lohbeck T. Optimal layout of tree networks using genetic algorithms. Eng. Optimiz. 1993, 22(1):27-48. EGOPAX, 0305-215X, 10.1080/03052159308941324.
252. Walters G.A. Savic D.A. Morley M. de Schaetzen W. Calibration of water distribution network models using genetic algorithms. Proc., 7th Int. Conf. on Hydraulic Engineering Software-Hydrosoft 98 1998, W. R. Blain, ed., Comp. Mechanics, Southampton, U.K.
253. Walters G.A. Smith D.K. Evolutionary design algorithm for optimal layout of tree networks. Eng. Optimiz. 1995, 24:261-281. EGOPAX, 0305-215X, 10.1080/03052159508941193,
254. Wang C.G. Jamieson D.G. An objective approach to regional wastewater treatment planning. Water Resour. Res. 2002, 38(3):1022. WRERAQ, 0043-1397, 10.1029/2000WR000062.
255. Wang J. Lu Z. Habu H. The SCE-UA to solution of constrained nonlinear problem. J. Hohai Univ. 2001, 29(3):46-50..
256. Wang M. Zheng C. Optimal remediation policy selection under general conditions. Ground Water 1997, 35(5):757-764. GRWAAP, 0017-467X, 10.1111/j.1745-6584.1997.tb00144.x.
257. Wang M. Zheng C. Groundwater management optimization using genetic algorithms and simulated annealing: Formulation and comparison. J. Am. Water Resour. Assoc. 1998, 34(3):519-530. JWRAF5, 1093-474X, 10.1111/j.1752-1688.1998.tb00951.x.
258. Wang Q.J. The genetic algorithm and its application to calibration of conceptual rainfall-runoff models. Water Resour. Res. 1991, 27(9):2467-2471. WRERAQ, 0043-1397, 10.1029/91WR01305.
259. Wardlaw R. Sharif M. Evaluation of genetic algorithms for optimal reservoir system operation. J. Water Resour. Plann. Manage. 1999, 125(1):25-33. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1999)125:1(25).
260. Wu J. Zheng C. Chien C. Zheng L. A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty. Adv. Water Resour. 2006, 29:899-911. AWREDI, 0309-1708, 10.1016/j.advwatres.2005.08.005,
261. Wu J.F. Zheng C.M. Chien C.C. Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions. J. Contam. Hydrol. 2005, 77(1-2):41-65. JCOHE6, 0169-7722, 10.1016/j.jconhyd.2004.11.006.
262. Wu J.F. Zhu X.Y. Liu J.L. Using genetic algorithm based simulated annealing penalty function to solve groundwater management model. Sci. China, Ser. E: Technol. Sci. 1999, 42(5):521-529. SCETFO, 1006-9321, 10.1007/BF02917406,
263. Wu Z.Y. Sage P. Water loss detection via genetic algorithm optimization-based model calibration. Proc., 8th Annual Water Distribution System Symp. 2006, 11. (CD-ROM), ASCE, Reston, Va.,
264. Wu Z.Y. Walski T. Self-adaptive penalty approach compared with other constraint-handling techniques for pipeline optimization. J. Water Resour. Plann. Manage. 2005, 131(3):181-192. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2005)131:3(181),
265. Yan S. Minsker B. Optimal groundwater remediation design using an adaptive neural network genetic algorithm. Water Resour. Res. 2006, 42:W05407. WRERAQ, 0043-1397, 10.1029/2005WR004303,
266. Yandamuri S.R. M. Srinivasan K. Bhallamudi S.M. Multiobjective optimal waste load allocation models for rivers using nondominated sorting genetic algorithm-II. J. Water Resour. Plann. Manage. 2006, 132(3):133-143. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2006)132:3(133),
267. Yapo P.O. Gupta H.V. Sorooshian S. Multi-objective global optimization for hydrologic models. J. Hydrol. 1998, 204:83-97. JHYDA7, 0022-1694, 10.1016/S0022-1694(97)00107-8,
268. Yeh C.-.H. Labadie J.W. Multiobjective watershed-level planning of storm-water detention basins. J. Water Resour. Plann. Manage. 1997, 123(6):336-343. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1997)123:6(336),
269. Yeh W.W.-.G. Review of parameter identification procedures in groundwater hydrology: The inverse problem. Water Resour. Res. 1986, 22(2):95-108. WRERAQ, 0043-1397, 10.1029/WR022i002p00095,
270. Yoon J. Shoemaker C. An improved real-coded GA for groundwater bioremediation. J. Comput. Civ. Eng. 2001, 15(3):224-231. JCCEE5, 0887-3801, 10.1061/(ASCE)0887-3801(2001)15:3(224),
271. Yoon J.H. Shoemaker C.A. Comparison of optimization methods for ground-water bioremediation. J. Water Resour. Plann. Manage. 1999, 125(1):54-63. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1999)125:1(54),
272. Yu K.P. Harrell L.J. Evaluation of constraint handling techniques for evolutionary algorithm-based watershed management. Proc., World Water and Environmental Resources Congress 2004, ASCE, Reston, Va.
273. Zahraie B. Kerachian R. Malekmohammadi B. Reservoir operation optimization using adaptive varying chromosome length genetic algorithm. Water Int. 2008, 33(3):380-391. WAINEL, 0250-8060, 10.1080/02508060802272804,
274. Zechman E.M. Ranjithan S. Evolutionary computation-based approach for model error correction and calibration. Adv. Water Resour. 2007a, 30(5):1360-1370. AWREDI, 0309-1708, 10.1016/j.advwatres.2006.11.013,
275. Zechman E.M. Ranjithan S. Generating alternatives using evolutionary algorithms for water resources and environmental management problems. J. Water Resour. Plann. Manage. 2007b, 133(2):156-165. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2007)133:2(156),
276. Zhang Y.Q. Pinder G.F. Herrera G.S. Least cost design of groundwater quality monitoring networks. Water Resour. Res. 2005, 41:W08412. WRERAQ, 0043-1397, 10.1029/2005WR003936,
277. Zheng C. ModGA documentation and user's guide. 1997. Rep. Prepared for the DuPont Company, Hydrogeology Group, Univ. of Alabama, Tuscaloosa, Ala.
278. Zheng C. Wang P.P. Parameter structure identification using tabu search and simulated annealing. Adv. Water Resour. 1996, 19(4):215-224. AWREDI, 0309-1708, 10.1016/0309-1708(96)00047-4,
279. Zheng C.M. Wang P.P. A field demonstration of the simulation optimization approach for remediation system design. Ground Water 2002, 40(3):258-266. GRWAAP, 0017-467X, 10.1111/j.1745-6584.2002.tb02653.x,
280. Zitzler E. Laumanns M. Thiele L. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Proc., Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001) 2002, 95-100. K. Giannakoglou, eds., Barcelona, Spain,
281. Zitzler E. Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 1999, 3(4):257-271. ITEVF5, 1089-778X, 10.1109/4235.797969,
282. Zou R. Lung W. Robust water quality model calibration using an alternating fitness genetic algorithm. J. Water Resour. Plann. Manage. 2004, 130(6):471-479. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(2004)130:6(471),