Genetic Algorithms, Optimization, and Evolutionary Modeling

Treatise on Geomorphology

Volumn 2, Issue , 2013, Pages 74-97

  D'Ambrosio, D ^a   Spataro, W ^a   Rongo, R ^a   Iovine, G G R ^b  

^a UNIVERSITY OF CALABRIA   (Italy)

^b CNR   (Italy)

Author keywords

Calibration; Cellular automata; Etna; Evolutionary algorithms; Genetic algorithms; Geomorphology; Modeling; Multiobjective optimization; NSGA II; Sarno; SCIARA; SCIDDICA; SWAT

Indexed keywords

EID: 84903431360     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-374739-6.00033-6     Document Type: Chapter

References (87)

1. Alba E., Tomassini M. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 2002, 6:443-462.
2. Amiri M., Esfahanian M., Hairi-Yazdi M.R., Esfahanian V. Minimization of power losses in hybrid electric vehicles in view of the prolonging of battery life. Journal of Power Sources 2009, 15:372-379.
3. Arnold J.G., Williams J.R., Srinivasan R., King K.W. SWAT: Soil and Water Assessment Tool 1999, USDA, Agricultural Research Service, Temple, TX.
4. Avolio M.V., D'Ambrosio D., Di Gregorio S., Lupiano V., Rongo R., Spataro W., Trunfio G.A. Evaluating cellular automata models by evolutionary multiobjective calibration. Cellular Automata, Proceedings of 8th International Conference on Cellular Automata for Research and Industry (ACRI 2008), Lecture Notes on Computer Science 5191 2008, 114-119. Springer, Berlin. H. Umeo, S. Morishita, K. Nishinari, T. Komatsuzaki, S. Bandini (Eds.).
5. Bekele E.G., Nicklow J.W. Multi-objective automatic calibration of SWAT using NSGA-II. Journal of Hydrology 2007, 341:165-176.
6. Berghman L., Goossens D., Leus R. Efficient solutions for Mastermind using genetic algorithms. Computers and Operations Research 2009, 36:1880-1885.
7. Beume N., Naujoks B., Emmerich M. SMS-EMOA: multiobjective selection based on dominated hypervolume. European Journal of Operational Research 2007, 181:1653-1669.
8. Beven K.J., Binley A.M. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processing 1992, 6:279-298.
9. Beven K.J., Freer J. Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology. Journal of Hydrology 2001, 249:11-29.
10. Blecic I., Cecchini A., Trunfio G.A. A decision support tool coupling a causal model and a multi-objective genetic algorithm. Applied Intelligence 2007, 26:125-137.
11. Blecic I., Cecchini A., Trunfio G.A. A multi-agent geosimulation infrastructure for planning. Studies in Computational Intelligence 2009, 176:237-253.
12. Brahim A., Mohamed N. The two-stage recombination operator and its application to the multiobjective 0/1 knapsack problem: a comparative study. Computers and Operations Research 2009, 36:3247-3262.
13. Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., Zitzler, E., 2007. Do additional objectives make a problem harder? Proceedings of 9th annual conference on Genetic and evolutionary computation (GECCO '07). ACM, New York, NY, 7-11 July, 2007.
14. Cantù-Paz E. Efficient and Accurate Parallel Genetic Algorithms 2000, Kluwer Academic Publishers, New York, NY, 162 pp.
15. Chang P.-C., Fan C.-Y., Dzan W.-Y. A CBR-based fuzzy decision tree approach for database classification. Expert Systems With Applications 2010, 37:214-225.
16. Chang T.-C., Chien Y.-H. The application of genetic algorithm in debris flow prediction. Environmental Geology 2007, 53:339-347.
17. Chen S., Lin S., Huang L., Hsu S. Strategy optimization for deductive games. European Journal of Operational Research 2007, 183:757-766.
18. Coello Coello C., Aguirre A., Zitzler E. Evolutionary multi-objective optimization. European Journal of Operational Research 2007, 181:1617-1619.
19. Coello Coello C.A., Van Veldhuizen D.A., Lamont G.B. Evolutionary Algorithms for Solving Multi-Objective Problems 2002, Kluwer Academic Publishers, New York, NY, 610 pp.
20. Corne, D., Knowles, J., Oates, M., 2000. The Pareto envelope-based selection algorithm for multiobjective optimization. Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature VI (PPSN-VI) Paris, France, 18-20 September, 2000, pp. 839-848.
21. Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J., 2001. PESA-II: region-based selection in evolutionary multiobjective optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01). Morgan Kaufmann, San Francisco, CA, USA, 7-11 July, 2001, pp. 283-290.
22. D'Ambrosio D., Iovine G., Spataro W., Miyamoto H. A macroscopic collisional model for debris-flows simulation. Environmental Modelling and Software 2007, 22:1417-1436.
23. D'Ambrosio D., Spataro W. Parallel evolutionary modelling of geological processes. Parallel Computing 2007, 33:186-212.
24. D'Ambrosio D., Spataro W., Iovine G. Parallel genetic algorithms for optimising cellular automata models of natural complex phenomena: an application to debris flows. Computers and Geosciences 2006, 32:861-875.
25. De Jong, K.A., 1975. Analysis of behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan.
26. De Jong K., Fogel D.B., Schwefel H.P. A history of evolutionary computation. Handbook of Evolutionary Computation 1997, A2.3:1-12. IOP Publishing and Oxford University Press, New York, NY. T. Back, D.B. Fogel, Z. Michalewicz, T. Baeck (Eds.).
27. Deb K. Multi-objective Optimization Using Evolutionary Algorithms 2001, Wiley, Chichester, 518 pp.
28. Deb K. Current trends in evolutionary multi-objective optimization. International Journal for Simulation and Multidisciplinary Design Optimization 2007, 1:1-8.
29. Deb K. Multi-Objective Optimization Using Evolutionary Algorithms 2009, Wiley, New York, NY, 536 pp.
30. Deb K., Agrawal R.B. Simulated binary crossover for continuous search space. Complex Systems 1995, 9:115-148.
31. Deb K., Beyer H. Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation 2001, 2:197-221.
32. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002, 6:182-197.
33. Del Prete M., Guadagno F.M., Hawkins A.B. Preliminary report on the landslides of 5 May 1998, Campania, Southern Italy. Bulletin of Engineering Geology and the Environment 1998, 57:113-129.
34. Di Gregorio S., Rongo R., Siciliano C., Sorriso-Valvo M., Spataro W. Mount Ontake landslide simulation by the cellular automata model SCIDDICA-3. Physics and Chemistry of the Earth-Part A 1999, 24:97-100.
35. Di Gregorio S., Serra R. An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata. Future Generation Computer Systems 1999, 16:259-271.
36. Edgeworth F.Y. Mathematical Physics 1881, P. Keagan, London, 150 pp.
37. Fogel L.J., Owens A.J., Walsh M.J. Artificial Intelligence through Simulated Evolution 1966, Wiley, Chichester, 170 pp.
38. Fonseca, C.M., Fleming, P.J., 1993. Genetic algorithms for multiobjective optimization: formulation, discussion, and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms. Urbana-Champaign, IL, USA, 7-22 July, 1993, pp. 416-423.
39. Goldberg D.E. Genetic Algorithms in Search. Optimization and Machine Learning 1989, Addison-Wesley, Boston, 372 pp.
40. Goldberg D.E., Richardson J. Genetic algorithm with sharing for multimodal function optimization. Genetic Algorithms and Their Applications. Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ, 28-31 July, 1987 1987, 41-49. Lawrence Erlbaum Associates, Hove, London. J.J. Grefenstette (Ed.).
41. Handl J., Knowles J. An evolutionary approach to multiobjective clustering. IEEE Transactions on Evolutionary Computation 2007, 11:56-76.
42. Holland J.H. Nonlinear environments permitting efficient adaptation. Proceedings of Computer and Information Sciences - II 1967, Academic Press, New York, NY, pp. 147-164.
43. Holland J.H. Adaptation in Natural and Artificial Systems 1975, University of Michigan Press, Ann Arbor, 228 pp.
44. Horn, J., Nafploitis, N., Goldberg, D.E., 1994. A niched Pareto genetic algorithm for multi-objective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. Orlando, Florida, USA, 27-29 June, 1994, pp. 82-87.
45. Iman R.L., Conover W.J. Small sample sensitivity analysis techniques for computer models, with an application to risk assessment. Communications in Statistics: Theory and Methods A 1980, 9:1749-1874.
46. Iovine G., D'Ambrosio D., Di Gregorio S. Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects. Geomorphology 2005, 66:287-303.
47. Jähne, M., Li, X., Branke, J., 2009. Evolutionary algorithms and multi-objectivization for the travelling salesman problem. Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO '09) ACM, Montéal, Canada, 8-12 July, 2009, pp. 595-602.
48. Jensen M.T. Helper-Objectives: using multi-objective evolutionary algorithms for single-objective optimisation. Journal of Mathematical Modelling and Algorithms 2004, 3:323-347.
49. Khu S.-T., Madsen H., Di Pierro F. Incorporating multiple observations for distributed hydrologic model calibration: an approach using a multi-objective evolutionary algorithm and clustering. Advances in Water Resources 2008, 31:1387-1398.
50. Knowles J.D., Corne D.W. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation 2000, 8:149-172.
51. Knowles, J.D., Watson, R.A., Corne, D., 2001. Reducing Local Optima in Single-Objective Problems by Multi-objectivization. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO '01). Zurich, Switzerland, 7-9 March, 2001, pp. 269-283.
52. Li Y., Zhang L., Liu Z., Wei W., Li S. Multi-objective optimization design of heavy forge framed hydraulic press driven by stiffness and mass. Journal of Mechanical Engineering 2010, 46:140-146.
53. Madsen H. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology 2000, 235:276-288.
54. Madsen H. Parameter estimation in distributed hydrological catchment modelling using automatic calibration with multiple objectives. Advances in Water Resources 2003, 26:205-216.
55. Madsen H., Wilson G., Ammentorp H.C. Comparison of different automated strategies for calibration of rainfall-runoff models. Journal of Hydrology 2002, 261:48-59.
56. Marocco D., Nolfi S. Emergence of communication in embodied agents evolved for the ability to solve a collective navigation problem. Connection Science 2007, 19:53-74.
57. McKay M.D., Conover W.J., Beckman R.J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 1979, 21:239-245.
58. Mendoza J.E., López M.E., Coello Coello C.A., López E.A. Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Generation, Transmission and Distribution 2009, 3:825-840.
59. Mitchell M. An Introduction to Genetic Algorithms 1996, MIT Press, Cambridge, 217 pp.
60. Morshed J., Kaluarachchi J.J. Application of artificial neural network and genetic algorithm in flow and transport simulations. Advances in Water Resources 1998, 22:145-158.
61. Muleta, M.K., 2003. A decision support system for the management of non-point source pollution from watersheds. PhD Thesis, College of Engineering, Southern Illinois University at Carbondale, Carbondale, IL, USA.
62. Muleta M.K., Nicklow J.W. Evolutionary algorithms for multiobjective evaluation of watershed management decisions. Journal of Hydroinformatics 2002, 4:83-97.
63. Muleta M.K., Nicklow J.W. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology 2005, 306:127-145.
64. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models. Part I - a discussion of principles. Journal of Hydrology 1970, 125:277-291.
65. Nicklow J.W., Muleta M.K. Watershed management technique to control sediment yield in agriculturally dominated areas. Water International 2001, 26:435-443.
66. Nicklow J.W., Ozkurt O., Bringer J.A. Control of channel bed morphology in large-scale river networks using a genetic algorithm. Water Resources Management 2003, 17:113-132.
67. Nolfi S., Floreano D. Evolutionary Robotics: The Biology. Intelligence, and Technology of Self-Organizing Machines (Intelligent Robotics and Autonomous Agents) 2000, MIT Press, Cambridge, 336 pp.
68. Nolfi S., Marocco D. Evolving robots able to visually discriminate between objects with different size. International Journal of Robotics and Automation 2002, 17:163-170.
69. Oh J.C. Promoting cooperation using kin biased conditional strategy in the iterated prisoner's dilemma game. Information Science 2001, 133:149-164.
70. Pareto V. Cours D'Economie Politique 1896, F. Rouge, Lausanne, Tome Premier, viii, 430 pp. Tome Second, 426 pp.
71. Peniak, M., Marocco, D., Cangelosi, A., 2009. Co-evolving controller and sensing abilities in a simulated mars rover explorer. Proceedings of IEEE Congress on Evolutionary Computation (CEC '09). Trondheim, Norway, 4983290, 18-21 May, 2009, pp. 2772-2779.
72. Rechenberg I. Evolutionsstrategie: Optinierung Technischer Systeme nach Prinzipien der Biologischen Evolution 1973, Fromman-Holzboog, Struttgart.
73. Reed P.M., Minsker B.S., Goldberg D.E. Designing a competent simple genetic algorithm for search and optimization. Water Resources Research 2000, 36:3757-3761.
74. Rongo R., Spataro W., D'Ambrosio D., Avolio M.V., Trunfio G.A., Di Gregorio S. Lava flow hazard evaluation through cellular automata and genetic algorithms: an application to Mt Etna Volcano. Fundamenta Informaticae 2008, 87:247-267.
75. Rudolph, G., Agapie, A., 2000. Convergence Properties of Some Multi-Objective Evolutionary Algorithms. Proceedings of the 2000 Conference on Evolutionary Computation, La Jolla, CA, USA, 16-19 July, 2000, pp. 1010-1016.
76. Schaffer J.D. Multiple objective optimization with vector evaluated genetic algorithms. PhD Thesis 1984, Vanderbilt University, The Netherlands.
77. Schaffer, J.D., 1985. Multiple objective optimization with vector evaluated genetic Algorithms. In: Genetic Algorithms and their Applications. Proceedings of the First International Conference on Genetic Algorithms. Lawrence Erlbaum, Pittsburgh, PA, USA, July 1985, pp. 93-100.
78. Singh A., Minsker B.S. Uncertainty-based multiobjective optimization of groundwater remediation design. Water Resources Research 2008, 44:W02404.
79. Spataro W., D'Ambrosio D., Rongo R., Trunfio G.A. An evolutionary approach for modelling lava flows through cellular automata. Cellular Automata, Proceedings of 6th International Conference on Cellular Automata for Research and Industry (ACRI 2004), Lecture Notes on Computer Science (LNCS) 3305 2004, 725-734. Springer, Berlin. P.M.A. Sloot, B. Chopard, A.G. Hoekstra (Eds.).
80. Srinivas N., Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 1994, 2:221-248.
81. Stone C., Bull L. Evolution of cellular automata with memory: the density classification task. BioSystems 2009, 97:108-116.
82. Tan K.C, Goh C.K. Handling uncertainties in evolutionary multi-objective optimization. Proceedings of the 2008 IEEE world conference on Computational intelligence, Lecture Notes in Computer Sciences (LNCS) 5050 2008, 262-292. Springer, Berlin. J.M. Zurada, G.G. Yen, J. Wang (Eds.).
83. Tanimoto J. Environmental dilemma game to establish a sustainable society dealing with an emergent value system. Physica D 2005, 200:1-24.
84. Tomassini M., Vanneschi L. Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two. Genetic Programming and Evolvable Machines 2009, 10:339-341.
85. Zitzler E., Laumanns M., Bleuler S. A tutorial on evolutionary multiobjective optimization. Metaheuristics for Multiobjective Optimisation, Lecture Notes in Economics and Mathematical Systems 535 2004, 3-37. Springer, Berlin. X. Gandibleux, M. Sevaux, K. Sørensen, V. T'kindt (Eds.).
86. Zitzler E., Laumanns M., Thiele L. PEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems. International Center for Numerical Methods in Engineering (CIMNE), Athens 2001, 95-100. K.C. Giannakoglou, D.T. Tsahalis, J. Périaux, K.D. Papailiou, T. Fogarty (Eds.).
87. Zitzler, E., Thiele, L., 1998. Multiobjective optimization using evolutionary algorithms - A comparative case study. Proceedings of Parallel Problem Solving from Nature V (PPSN-V). Amsterdam, The Netherland, 27-30 September, 1998, pp. 292-301.