A unified taxonomy of hybrid metaheuristics with mathematical programming, constraint programming and machine learning

Studies in Computational Intelligence

Volumn 434, Issue , 2013, Pages 3-76

  Talbi, El Ghazali ^a  

^a UNIV LILLE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84867436970     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-30671-6_1     Document Type: Article

References (183)

1. Abbattista, F., Abbattista, N., Caponetti, L.: An evolutionary and cooperative agent model for optimization. In: IEEE Int. Conf. on Evolutionary Computation, ICEC 1995, Perth, Australia, pp. 668-671 (December 1995)
2. Abramson, D., Logothetis, P., Postula, A., Randall, M.: Application specific computers for combinatorial optimisation. In: Australien Computer Architecture Workshop, Sydney, Australia (February 1997)
3. Abramson, D.A.: A very high speed architecture to support simulated annealing. IEEE Computer 25, 27-34 (1992)
4. Aggarwal, C.C., Orlin, J.B., Tai, R.P.: An optimized crossover for the maximum independent set. Operations Research 45, 226-234 (1997) (Pubitemid 127684701)
5. Agrafiotis, D.K.: Multiobjective optimization of combinatorial libraries. Technical report, IBM J. Res. and Dev. (2001)
6. Aiex, R.M., Binato, S., Ramakrishna, R.S.: Parallel GRASP with path relinking for job shop scheduling. Parallel Computing 29, 393-430 (2003)
7. Al-Yamani, A., Sait, S., Youssef, H.: Parallelizing tabu search on a cluster of heterogeneous workstations. Journal of Heuristics 8(3), 277-304 (2002) (Pubitemid 34536962)
8. Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA Journal on Computing 3, 149-156 (1991)
9. Apt, K.: Principles of constraint programming. Cambridge University Press (2003)
10. Augerat, P., Belenguer, J.M., Benavent, E., Corberan, A., Naddef, D.: Separating capacity constraints in the CVRP using tabu search. European Journal of Operational Research 106(2), 546-557 (1998) (Pubitemid 128399668)
11. Balas, E.,Niehaus,W.:Optimized crossover-based genetic algorithms for the maximum cardinality and maximum weight clique problems. Journal of Heuristics 4(2), 107-122 (1998) (Pubitemid 128514872)
12. Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., Vance, P.H.: Branch-and-price: column generation for huge integer programs. Operations Research 46(316) (1998)
13. Basseur, M., Lemesre, J., Dhaenens, C., Talbi, E.-G.: Cooperation Between Branch and Bound and Evolutionary Approaches to Solve a Bi-Objective Flow Shop Problem. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 72-86. Springer, Heidelberg (2004)
14. Basseur, M., Seynhaeve, F., Talbi, E.-G.: Design of multi-objective evolutionary algorithms: Application to the flow-shop scheduling problem. In: Congress on Evolutionary Computation, CEC 2002, Honolulu, Hawaii, USA, pp. 1151-1156 (May 2002)
15. Basseur, M., Seynhaeve, F., Talbi, E.-G.: Adaptive mechanisms for multi-objective evolutionary algorithms. In: Congress on Engineering in System Application, CESA 2003, Lille, France, pp. 72-86 (2003)
16. Basseur, M., Seynhaeve, F., Talbi, E.-G.: Path Relinking in Pareto Multi-Objective Genetic Algorithms. In: Coello Coello, C.A., Aguirre, A.H., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 120-134. Springer, Heidelberg (2005) (Pubitemid 41251760)
17. Beasley, J.E.: OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069-1072 (1990)
18. Beausoleil, R.P.: Mutiple criteria scatter search. In: 4th Metaheuristics International Conference (MIC 2001), Porto, Portugal, pp. 539-544 (2001)
19. Belding, T.: The distributed genetic algorithm revisted. In: Eshelmann, D. (ed.) Sixth Int. Conf. on Genetic Algorithms. Morgan Kaufmann, San Mateo (1995)
20. Belew, R.K., McInerny, J., Schraudolph, N.N.: Evolving networks: Using genetic algorithms with connectionist learning. In: Langton, C.G., Taylor, C., Doyne Farmer, J.D., Rasmussen, S. (eds.) Second Conf. on Artificial Life, pp. 511-548. Addison-Wesley, USA (1991)
21. Bellman, R.: Dynamic programming. Princeton University Press, NJ (1957)
22. Benders, J.F.: Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik 4, 238-252 (1962)
23. Bertsekas, D.P.: Network optimization: Continuous and discrete models. Athena Scientific, MA (1998)
24. Boese, K.D.: Models for iterative global optimization. PhD thesis. University of California, Los Angeles (1996)
25. Boese, K.D., Kahng, A.B., Muddu, S.: New adaptive multi-start techniques for combinatorial global optimizations. Operations Research Letters 16(2), 101-113 (1994)
26. Braun, H.: On Solving Traveling Salesman Problems by Genetic Algorithms. In: Schwefel, H.-P.,M̈anner, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 129-133. Springer, Heidelberg (1991)
27. Burke, E.K., Cowling, P.I., Keuthen, R.: Effective Local and Guided Variable Neighbourhood Search Methods for the Asymmetric Travelling Salesman Problem. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, Evo-COP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 203-312. Springer, Heidelberg (2001)
28. Burke, E.K., Kendall, G., Newall, J., Hart, E., Ross, P., Schulemburg, S.: Hyperheuristics: An emerging direction in modern search technology. In: Handbook of Metaheuristics. Kluwer Academic Publishers (2003)
29. Burke, E.K., Landa Silva, J.D., Soubeiga, E.:Hyperheuristic approaches formultiobjective optimisation. In: 5th Metaheuristics International Conference (MIC 2003), Kyoto, Japan (August 2003)
30. Caseau, Y., Laburthe, F.: Disjunctive scheduling with task intervals. Technical Report LIENS-95-25, Ecole Normale Suṕerieure de Paris, France (1995)
31. Caseau, Y., Laburthe, F.: Heuristics for large constrained routing problems. Journal of Heuristics 5, 281-303 (1999) (Pubitemid 30505843)
32. Cesta, A., Cortellessa, G., Oddi, A., Policella, N., Susi, A.: A Constraint-Based Architecture for Flexible Support to Activity Scheduling. In: Esposito, F. (ed.) AI*IA 2001. LNCS (LNAI), vol. 2175, pp. 369-390. Springer, Heidelberg (2001)
33. Chabrier, A., Danna, E., Le Pape, C.: Cooṕeration entre ǵeńeration de colonnes sans cycle et recherche locale appliqúee au routage de v́ehicules. In: Huitìemes Jourńees Nationales sur la Ŕesolution de Probl̀emes NP-Complets, JNPC 2002, Nice, France (May 2002)
34. Chang, C.S., Huang, J.S.: Optimal multiobjective SVC planning for voltage stability enhancement. IEE Proceedings on Generation, Transmission and Distribution 145(2), 203-209 (1998) (Pubitemid 128729202)
35. Chelouah, R., Siarry, P.: A hybrid method combining continuous tabu search and Nelder-Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research 161(3), 636-654 (2004)
36. Chen, H., Flann, N.S.: Parallel simulated annealing and genetic algorithms: A space of hybrid methods. In: Davidor, Y., Schwefel, H.-P., Manner, R. (eds.) Third Conf. on Parallel Problem Solving from Nature, PPSN 1994, Jerusalem, Israel, pp. 428-436. Springer (October 1994)
37. Chu, P.C.: A genetic algorithm approach for combinatorial optimization problems. PhD thesis. University of London, London, UK (1997)
38. Chvatal, V.: A greedy heuristic for the set covering problem.Mathematics of Operations Research 4(3), 233-235 (1979)
39. Clearwater, S.H., Hogg, T., Huberman, B.A.: Cooperative problem solving. In: Huberman, B.A. (ed.) Computation: The Micro and the Macro View, pp. 33-70. World Scientific (1992)
40. Clearwater, S.H., Huberman, B.A., Hogg, T.: Cooperative solution of constraint satisfaction problems. Science 254, 1181-1183 (1991) (Pubitemid 21917445)
41. Cohoon, J., Hedge, S., Martin, W., Richards, D.: Punctuated equilibria: A parallel genetic algorithm. In: Grefenstette, J.J. (ed.) Second Int. Conf. on Genetic Algorithms, pp. 148-154. MIT, Cambridge (1987)
42. Cohoon, J.P., Martin, W.N., Richards, D.S.: Genetic Algorithms and Punctuated Equilibria. In: Schwefel, H.-P.,M̈anner, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 134-141. Springer, Heidelberg (1991)
43. Cohoon, J.P., Martin, W.N., Richards, D.S.: A multi-population genetic algorithm for solving the k-partition problem on hypercubes. In: Belew, R.K., Booker, L.B. (eds.) Fourth Int. Conf. on Genetic Algorithms, pp. 244-248. Morgan Kaufmann, San Mateo (1991)
44. Cook,W., Seymour, P.: Tour merging via branch-decomposition. INFORMS Journal on Computing 15(3), 233-248 (2003) (Pubitemid 39042719)
45. Coyne, J., Paton, R.: Genetic Algorithms and Directed Adaptation. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 103-114. Springer, Heidelberg (1994)
46. Crainic, T.G., Nguyen, A.T., Gendreau, M.: Cooperative multi-thread parallel tabu search with evolutionary adaptive memory. In: 2nd Int. Conf. on Metaheuristics, Sophia Antipolis, France (July 1997)
47. Crainic, T.G., Toulouse, M., Gendreau, M.: Synchronous tabu search parallelization strategies for multi-commodity location-allocation with balancing requirements. OR Spektrum 17, 113-123 (1995)
48. Crainic, T.G., Toulouse, M.: Parallel strategies for metaheuristics. In: Glover, F.W., Kochenberger, G.A. (eds.) Handbook of Metaheuristics, pp. 475-513. Springer (2003)
49. Cung, V.-D., Mautor, T., Michelon, P., Tavares, A.: A scatter search based approach for the quadratic assignment problem. In: IEEE Int. Conf. on Evolutionary Computation, ICEC 1997, Indianapolis, USA (April 1997)
50. Cung, V.-D., Mautor, T., Michelon, P., Tavares, A.: Recherche disperśee parall̀ele. In: Deuxìeme Congŕes de la Socíet́e Francaise de Recherche Oṕerationnelle et d'Aide ̀a la D́ecision, ROADEF 1999, Autrans, France (January 1999)
51. Dalboni, F.L., Ochi, L.S., Drummond, L.M.D.: On improving evolutionary algorithms by using data mining for the oil collector vehicle routing problem. In: Int. Network Optimization Conf., INOC 2003, Paris, France (October 2003)
52. Davis, L.: Job-shop scheduling with genetic algorithms. In: Grefenstette, J.J. (ed.) Int. Conf. on Genetic Algorithms and their Applications, Pittsburgh, pp. 136-140 (1985)
53. Deb, K., Goel, T.: A hybrid Multi-Objective Evolutionary Approach to Engineering Shape Design. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 385-399. Springer, Heidelberg (2001) (Pubitemid 33241401)
54. Delisle, P., Krajecki, M., Gravel, M., Gagńe, C.: Parallel implementation of an ant colony optimization metaheuristic with OpenMP. In: 3rd European Workshop on OpenMP (EWOMP 2001), pp. 8-12 (2001)
55. Dimitrescu, I., Stutzle, T.: Combinations of local search and exact algorithms. In: Evo Workshops, pp. 211-223 (2003)
56. Dowsland, K.A.: Nurse scheduling with tabu search and strategic oscillation. European Journal of Operational Research 106, 393-407 (1998) (Pubitemid 128399659)
57. Dowsland, K.A., Herbert, E.A., Kendall, G.: Using tree search bounds to enhance a genetic algorithm approach to two rectangle packing problems. European Journal of Operational Research 168(2), 390-402 (2006) (Pubitemid 41329854)
58. Dowsland, K.A., Thomson, J.M.: Solving a nurse scheduling problem with knapsacks, networks and tabu search. Journal of Operational Research Society 51, 825-833 (2000)
59. Eby, D., Averill, R., Punch, W., Goodman, E.: Evaluation of injection island model GA performance on flywheel design optimization. In: Int. Conf on Adaptive Computing in Design and Manufacturing, Devon, UK, pp. 121-136. Springer (1998)
60. Engelmore, R.S., Morgan, A.: Blackboard systems. Addison-Wesley (1988)
61. De Falco, I., Del Balio, R., Tarantino, E.: An analysis of parallel heuristics for task allocation in multicomputers. Computing 59(3), 259-275 (1997) (Pubitemid 127653310)
62. De Falco, I., Del Balio, R., Tarantino, E., Vaccaro, R.: Improving search by incorporating evolution principles in parallel tabu search. In: IEEE Conference on Evolutionary Computation, pp. 823-828 (1994)
63. Federgruen, A., Tzur, M.: Time-partitioning heuristics: Application to one warehouse, multi-item, multi-retailer lot-sizing problems. Naval Research Logistics 46, 463-486 (1999) (Pubitemid 129584916)
64. Feo, T.A., Resende, M.G.C.: Greedy randomized adaptive search procedures. Journal of Global Optimization 6, 109-133 (1995)
65. Feo, T.A., Resende, M.G.C., Smith, S.H.: A greedy randomized adaptive search procedure for maximum independent set. Operations Research 42, 860-878 (1994)
66. Feo, T.A., Venkatraman, K., Bard, J.F.:A GRASP for a difficult single machine scheduling problem. Computers and Operations Research 18, 635-643 (1991)
67. Filho, G.R., Lorena, L.A.N.: Constructive genetic algorithm and column generation: An application to graph coloring. In: APORS 2000 Conf. of the Association of the Asian-Pacific Operations Research Societies within IFORS (2000)
68. Fischetti, M., Lodi, A.: Local branching. Mathematical Programming B 98(23-47) (2003)
69. Fisher,M.L.: An application oriented guide to lagrangian relaxation. Interfaces 15, 399-404 (1985)
70. Fleurent, C., Ferland, J.A.: Genetic hybrids for the quadratic assignment problem. DIMACS Series in Discrete Mathematics and Theoretical Computer Science 16, 173-188 (1994)
71. Fleurent, C., Ferland, J.A.: Genetic and hybrid algorithms for graph coloring. Annals of Operations Research 63(3), 437-461 (1996) (Pubitemid 126729251)
72. Focacci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Handbook of Metaheuristics. International Series in Operations Research and Management Science. Kluwer Academic Publishers, Norwell (2002)
73. Fonlupt, C., Robillard, D., Preux, P., Talbi, E.-G.: Fitness landscape and performance of metaheuristics. In: Meta-Heuristics-Advances and Trends in Local Search Paradigms for Optimization, pp. 255-266. Kluwer Academic Press (1999)
74. Gao, B., Liu, T.-Y., Feng, G., Qin, T., Cheng, Q.-S., Ma, W.-Y.: Hierarchical taxonomy preparation for text categorization using consistent bipartite spectral graph copartitioning. IEEE Transactions on Knowledge and Data Engineering 17(9), 1263-1273 (2005) (Pubitemid 46488942)
75. Gen, M., Lin, L.: Multiobjective hybrid genetic algorithm for bicriteria network design problem. In: The 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Cairns, Australia, pp. 73-82 (December 2004)
76. Gendreau, M., Laporte, G., Semet, F.: The covering tour problem. Operations Research 45, 568-576 (1997) (Pubitemid 127684731)
77. Gilmore, P.C.,Gomory, R.E.: A linear programming approach to the cutting stock problem. Operations Research 9, 849-859 (1961)
78. Ginsberg, M.L.: Dynamic backtracking. Journal of Artificial Intelligence Research 1, 25-46 (1993)
79. Golden, B., Pepper, J., Vossen, T.: Using genetic algorithms for setting parameter values in heuristic search. Intelligent Engineering Systems Through Artificial Neural Networks 1, 9-32 (1998)
80. Golovkin, I.E., Louis, S.J., Mancini, R.C.: Parallel implementation of niched Pareto genetic algorithm code for X-ray plasma spectroscopy. In: Congress on Evolutionary Computation, CEC 2002, pp. 1820-1824 (2002)
81. Gomory, R.E.: Outline of an algorithm for integer solutions to linear programs. Bulletin AMS 64, 275-278 (1958)
82. Grefenstette, J.J.: Incorporating problem specific knowledge into genetic algorithms. In: Davis, L. (ed.) Genetic Algorithms and Simulated Annealing, Research Notes in Artificial Intelligence, pp. 42-60. Morgan Kaufmann, San Mateo (1987)
83. Gutin, G.M.: Exponential neighborhood local search for the traveling salesman problem. Computers and Operations Research 26(4), 313-320 (1999)
84. Habet, D., Li, C.-M., Devendeville, L., Vasquez, M.: A Hybrid Approach for SAT. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 172-184. Springer, Heidelberg (2002)
85. Hansen, P., Mladenovic, M., Perez-Britos, D.: Variable neighborhood decomposition search. Journal of Heuristics 7(4), 330-350 (2001)
86. Hart, W.E.: Adaptive global optimization with local search. PhD thesis. University of California, San Diego (1994)
87. Harvey, W.D., Ginsberg, M.L.: Limited discrepancy search. In: IJCAI Int. Joint Conference on Artificial Intelligence, pp. 607-613. Morgan Kaufmann (1997)
88. Hindi, K.S., Fleszar, K., Charalambous, C.: An effective heuristic for the CLSP with setup times. Journal of the Operations Research Society 54, 490-498 (2003)
89. Hinterding, R., Michalewicz, Z., Eiben, A.-E.: Adaptation in evolutionary computation: A survey. In: Proceedings of the IEEE Conference on Evolutionary Computation, Indianapolis, USA, pp. 65-69 (April 1997)
90. Hogg, T., Williams, C.: Solving the really hard problems with cooperative search. In: 11th Conf. on Artificial Intelligemce AAAI 1993, pp. 231-236. AAAI Press (1993)
91. Hong, T.-P.,Wang, H.-S., Chen, W.-C.: Simultaneous applying multiple mutation operators in genetic algorithm. Journal of Heuristics 6(4), 439-455 (2000)
92. Huberman, B.A.: The performance of cooperative processes. Physica D 42, 38-47 (1990)
93. Husbands, P., Mill, F., Warrington, S.: Genetic Algorithms, Production Plan Optimisation and Scheduling. In: Schwefel, H.-P., M̈anner, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 80-84. Springer, Heidelberg (1991)
94. Ishibuchi, H.,Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 28(3), 392-403 (1998) (Pubitemid 128748638)
95. Jahuira, C.A.R., Cuadros-Vargas, E.: Solving the TSP by mixing GAs with minimal spanning trees. In: First Int. Conf. of the Peruvian Computer Society, Lima, Peru, pp. 123-132 (2003)
96. Jaszkiewicz, A.: Genetic local search for multiple objective combinatorial optimization. Technical Report RA-014/98. Institute of Computing Science, Poznan University of Technology (1998)
97. Jaszkiewicz, J.: Path relinking for multiple objective combinatorial optimization: TSP case study. In: The 16th Mini-EURO Conference and 10th Meeting of EWGT (Euro Working Group Transportation) (2005)
98. Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3-12 (2005) (Pubitemid 40097880)
99. Jin, Y., Sendhoff, B.: Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 688-699. Springer, Heidelberg (2004) (Pubitemid 39747263)
100. Jog, P., Suh, J.Y., Van Gucht, D.: The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem. In: 3rd Int. Conf. Genetic Algorithms. Morgan Kaufmann, USA (1989)
101. Jones, B.R., Crossley, W.A., Lyrintzis, A.S.: Aerodynamic and aeroacoustic optimization of airfoils via parallel genetic algorithm. Journal of Aircraft 37(6), 1088-1098 (2000)
102. Jourdan, L., Basseur, M., Talbi, E.-G.: Hybridizing exact methods and metaheuristics: A taxonomy. European Journal of Operational Research (2008) (to appear)
103. Jourdan, L., Corne, D.W., Savic, D.A., Walters, G.A.: Preliminary Investigation of the 'Learnable Evolution Model' for Faster/Better Multiobjective Water Systems Design. In: Coello Coello, C.A., Herńandez Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 841-855. Springer, Heidelberg (2005) (Pubitemid 41251809)
104. Jourdan, L., Dhaenens, C., Talbi, E.-G.: Using Datamining Techniques to Help Metaheuristics: A Short Survey. In: Almeida, F., Blesa Aguilera, M.J., Blum, C., Moreno Vega, J.M., Ṕerez Ṕerez, M., Roli, A., Sampels, M. (eds.) HM 2006. LNCS, vol. 4030, pp. 57-69. Springer, Heidelberg (2006) (Pubitemid 44577448)
105. Jozefowiez, N.: Mod́elisation et ŕesolution approch́ee de probl̀emes de tourńees multiobjectif. PhD thesis. University of Lille, Lille, France (2004)
106. Jozefowiez, N., Semet, F., Talbi, E.-G.: Parallel and Hybrid Models forMulti-Objective Optimization: Application to the Vehicle Routing Problem. In: Guerv́os, J.J.M., Adamidis, P.A., Beyer, H.-G., Ferńandez- Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 271-280. Springer, Heidelberg (2002)
107. Jozefowiez, N., Semet, F., Talbi, E.-G.: The bi-objective covering tour problem. Computers and Operations Research 34, 1929-1943 (2007)
108. Juenger, M., Reinelt, G., Thienel, S.: Practical problem solving with cutting plane algorithms in combinatorial optimization. DIMACS Series in Discrete Mathematics and Theoretical Computer Science 20, 111-152 (1995)
109. Kamarainen, O., El Sakkout, H.: Local Probing Applied to Scheduling. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 155-171. Springer, Heidelberg (2002)
110. Karp, R.M.: Probabilistic analysis of partitioning algorithms for the traveling salesman problem in the plane. Mathematics of Operations Research 2, 209-224 (1977)
111. Kim, H., Hayashi, Y., Nara, K.: The performance of hybridized algorithm of genetic algorithm simulated annealing and tabu search for thermal unitmaintenance scheduling. In: 2nd IEEE Conf. on Evolutionary Computation, ICEC 1995, Perth, Australia, pp. 114-119 (December 1995)
112. Kim, H.-S., Cho, S.-B.: An efficient genetic algorithm with less fitness evaluation by clustering. In: Congress on Evolutionary Computation, CEC 2001, pp. 887-894. IEEE Press (2001) (Pubitemid 32806235)
113. Kostikas, K., Fragakis, C.: Genetic Programming Applied to Mixed Integer Programming. In: Keijzer, M., O'Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 113-124. Springer, Heidelberg (2004)
114. Koza, J., Andre, D.: Parallel genetic programming on a network of transputers. Technical Report CS-TR-95-1542. Stanford University (1995)
115. Krueger, M.: Ḿethodes d'analyse d'algorithmes d'optimisation stochastiques ̀a l'aide d'algorithmes ǵeńetiques. PhD thesis, Ecole Nationale Sup̀erieure des T́eĺecommunications, Paris, France (December 1993)
116. Lemesre, J., Dhaenens, C., Talbi, E.-G.: An exact parallel method for a bi-objective permutation flowshop problem. European Journal of Operational Research (EJOR) 177(3), 1641-1655 (2007) (Pubitemid 44709442)
117. Levine, D.: A parallel genetic algorithm for the set partitioning problem. PhD thesis. Argonne National Laboratory, Illinois Institute of Technology, Argonne, USA (May 1994)
118. Lin, F.T., Kao, C.Y., Hsu, C.C.: Incorporating genetic algorithms into simulated annealing. In: Proc. of the Fourth Int. Symp. on AI, pp. 290-297 (1991)
119. Louis, S.J.: Genetic learning from experiences. In: Congress on Evolutionary Computations, CEC 2003, Australia, pp. 2118-2125 (2003)
120. Lourenco, H.R.: Job-shop scheduling: Computational study of local search and largestep optimization methods. European Journal of Operational Research 83, 347-367 (1995)
121. Mahfoud, S.W., Goldberg, D.E.: Parallel recombinative simulated annealing: A genetic algorithm. Parallel Computing 21, 1-28 (1995)
122. Maniezzo, V.: Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing 11(4), 358-369 (1999)
123. Mariano, C.E.,Morales, E.: A multiple objective ant-q algorithm for the design of water distribution irrigation networks. In: First Int. Workshop on Ant Colony Optimization, ANTS 1998, Brussels, Belgium (1998)
124. Martin, O.C., Otto, S.W., Felten, E.W.: Large-step markov chains for the TSP: Incorporating local search heuristics. Operation Research Letters 11, 219-224 (1992)
125. Mautor, T., Michelon, P.:Mimausa: A new hybrid method combining exact solution and local search. In: Second Int. Conf. on Metaheuristics, Sophia-Antipolis, France (1997)
126. Meunier, H., Talbi, E.-G., Reininger, P.: A multiobjective genetic algorithm for radio network optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), La Jolla, CA, USA, pp. 317-324. IEEE Press (2000)
127. Michalski, R.S.: Learnable evolution model: Evolutionary processes guided by machine learning. Machine Learning 38(1), 9-40 (2000) (Pubitemid 30576113)
128. Minsky, M.: Negative expertise. International Journal of Expert Systems 7(1), 13-19 (1994)
129. Nagar, A., Heragu, S.S., Haddock, J.: A metaheuristic algorithm for a bi-criteria scheduling problem. Annals of Operations Research 63, 397-414 (1995)
130. Narayek, A., Smith, S., Ohler, C.: Integrating local search advice into a refinment search solver (or not). In: CP 2003 Workshop on Cooperative Constraint Problem Solvers, pp. 29-43 (2003)
131. Nemhauser, G., Wolsey, L.: Integer and combinatorial optimization. Wiley (1999)
132. Nissen, V.: Solving the quadratic assignment problem with clues from nature. IEEE Transactions on Neural Networks 5(1), 66-72 (1994)
133. Nuijten,W., Le Pape, C.: Constraint based job scheduling with ILOG scheduler. Journal of Heuristics 3, 271-286 (1998) (Pubitemid 128514119)
134. Nwana, V., Darby-Dowman, K., Mitra, G.: A cooperative parallel heuristic for mixed zero-one linear programming. European Journal of Operational Research 164, 12-23 (2005)
135. O'Reilly, U.-M., Oppacher, F.: Hybridized crossover-based techniques for program discovery. In: IEEE Int. Conf. on Evolutionary Computation, ICEC 1995, Perth, Australia, pp. 573-578 (December 1995)
136. Patterson, R., Rolland, E., Pirkul, H.: A memory adaptive reasoning technique for solving the capacitated minimum spanning tree problem. Journal of Heuristics 5, 159-180 (1999)
137. Pesant, G., Gendreau, M.: A view of local search in constraint programming. Journal of Heuristics 5, 255-279 (1999) (Pubitemid 30505844)
138. Potts, C.N., Velde, S.L.: Dynasearch-iterative local improvement by dynamic programming. Technical Report TR. University of Twente, Netherlands (1995)
139. Prestwich, S.: Combining the scalability of local search with the pruning techniques of systematic search. Annals of Operations Research 115, 51-72 (2002) (Pubitemid 37132208)
140. Puchinger, J., Raidl, G.R.: Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification. In: Mira, J., Á lvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 41-53. Springer, Heidelberg (2005) (Pubitemid 41425240)
141. Ramsey, C.L., Grefenstette, J.J.: Case-based initialization of genetic algorithms. In: Fifth Int. Conf. on Genetic Algorithms, pp. 84-91 (1993)
142. Rasheed, K., Vattam, S., Ni, X.: Comparison of methods for developing dynamic reduced models for design optimization. In: CEC 2002 Congress on Evolutionary Computation, pp. 390-395 (2002)
143. Renders, J.-M., Bersini, H.: Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways. In: First IEEE International Conference on Evolutionary Computation, pp. 312-317 (1994)
144. Reynolds, R.G., Michalewicz, Z., Peng, B.: Cultural algorithms: Computational modeling of how cultures learn to solve problems-an engineering example. Cybernetics and Systems 36(8), 753-771 (2005) (Pubitemid 41804872)
145. Ribeiro, M., Plastino, A.,Martins, S.: Hybridization of GRASP metaheuristic with data mining techniques. Journal of Mathematical Modelling and Algorithms 5(1), 23-41 (2006) (Pubitemid 43473948)
146. Rosing, K.E., ReVelle, C.S.: Heuristic concentration: Two stage solution construction. European Journal of Operational Research 97(1), 75-86 (1997) (Pubitemid 127369266)
147. Rowe, J., Vinsen, K., Marvin, N.: Parallel GAs for multiobjective functions. In: Proc. of the 2nd NordicWorkshop on Genetic Algorithms and Their Applications (2NWGA), pp. 61-70 (1996)
148. Rumelhart, D.E., Hinton, G.E.,Williams, R.J.: Learning representations by backpropagating errors. Nature 323, 533-536 (1986)
149. Salami, M., Cain, G.: Genetic algorithm processor on reprogrammable architectures. In: Fifth Annual Conference on Evolutionary Programming, EP 1996. MIT Press, San Diego (1996)
150. Sebag, M., Schoenauer, M., Ravise, C.: Toward civilized evolution: Developing inhibitions. In: B̈ack, T. (ed.) Seventh Int. Conf. on Genetic Algorithms, pp. 291-298 (1997)
151. Sefraoui, M., Periaux, J.: A Hierarchical Genetic Algorithm Using Multiple Models for Optimization. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 879-888. Springer, Heidelberg (2000)
152. Sellmann, M., Anśotegui, C.: Disco-novo-gogo: Integrating local search and complete search with restarts. In: The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, Boston, USA (2006)
153. Shahookar, K., Mazumder, P.: A genetic approach to standard cell placement using meta-genetic parameter optimization. IEEE Trans. on Computer-Aided Design 9(5), 500-511 (1990) (Pubitemid 20724379)
154. Shaw, P.: Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417-431. Springer, Heidelberg (1998) (Pubitemid 128151602)
155. Sprave, J.: A unified model of non-panmictic population structures in evolutionary algorithms. In: Proc. of the 1999 Congress on Evolutionary Computation, Piscataway, NJ, vol. 2, pp. 1384-1391. IEEE Press (1999)
156. Stutzle, T., Hoos, H.H.: The MAX-MIN ant system and local search for combinatorial optimization problems: Towards adaptive tools for global optimization. In: 2nd Int. Conf. on Metaheuristics, Sophia Antipolis, France, pp. 191-193. INRIA (July 1997)
157. Suh, J.Y., Van Gucht, D.: Incorporating heuristic information into genetic search. In: 2nd Int. Conf. Genetic Algorithms, pp. 100-107. Lawrence Erlbaum Associates, USA (1987)
158. Taillard, E.: Parallel iterative search methods for vehicle routing problem. Networks 23, 661-673 (1993)
159. Taillard, E.: Heuristic methods for large centroid clustering problems. Journal of Heuristics 9(1), 51-74 (2003)
160. Taillard, E., Voss, S.: POPMUSIC: Partial optimization metaheuristic under special intensification conditions. In: Essays and Surveys in Metaheuristics, pp. 613-629. Kluwer Academic Publishers (2002)
161. Taillard, E.D., Gambardella, L.: Adaptive memories for the quadratic assignment problem. Technical Report 87-97. IDSIA, Lugano, Switzerland (1997)
162. Taillard, E.D., Gambardella, L.M., Gendreau, M., Potvin, J.-Y.: Adaptive memory programming: a unified view of metaheuristics. European Journal of Operational Research 135(1), 1-16 (2001) (Pubitemid 32779931)
163. Talbi, E.-G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8, 541-564 (2002) (Pubitemid 34950326)
164. Talbi, E.-G., Bachelet, V.: COSEARCH: A parallel cooperative metaheuristic. Journal of Mathematical Modelling and Algorithms (JMMA) 5(2), 5-22 (2006) (Pubitemid 43473947)
165. Talbi, E.-G., Fonlupt, C., Preux, P., Robillard, D.: Paysages de probl̀emes d'optimisation et performances des ḿeta-heuristiques. In: Premier Congŕes de la Socíet́e Francaise de Recherche Oṕerationnelle et Aide ̀a la D́ecision ROAD, Paris, France (January 1998)
166. Talbi, E.G., Muntean, T., Samarandache, I.: Hybridation des algorithmes ǵeńetiques avec la recherche tabou. In: Evolution Artificielle, EA 1994, Toulouse, France (September 1994)
167. Talbi, E.-G., Rahoual, M., Mabed, M.H., Dhaenens, C.: A Hybrid Evolutionary Approach for Multicriteria Optimization Problems: Application to the Flow Shop. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 416-428. Springer, Heidelberg (2001) (Pubitemid 33241403)
168. Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. Journal of Heuristics 4(4), 295-321 (1998) (Pubitemid 128514318)
169. Tamura, H., Hirahara, A., Hatono, I., Umano, M.: An approximate solution method for combinatorial optimization-hybrid approach of genetic algorithm and lagrangean relaxation method. Trans. Soc. Instrum. Control Engineering 130, 329-336 (1994)
170. Tanese, R.: Parallel genetic algorithms for a hypercube. In: Proc. of the Second Int. Conf. on Genetic Algorithms, pp. 177-183. MIT, Cambridge (1987)
171. Thiel, J., Voss, S.: Some experiences on solving multiconstraint zero-one knapsack problems with genetic algorithms. INFOR 32(4), 226-242 (1994)
172. Toulouse, M., Crainic, T., Gendreau, M.: Communication issues in designing cooperative multi-thread parallel searches. In: Osman, I.H., Kelly, J.P. (eds.) Meta-Heuristics: Theory and Applications, pp. 501-522. Kluwer Academic Publishers (1996)
173. Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2), 161-184 (1998) (Pubitemid 128692901)
174. Ulder, N.L.J., Aarts, E.H.L., Bandelt, H.-J., Van Laarhoven, P.J.M., Pesch, E.: Genetic Local Search Algorithms for the Traveling Salesman Problem. In: Schwefel, H.-P., M̈anner, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 109-116. Springer, Heidelberg (1991)
175. Vasquez, M., Hao, J.-K.: A hybrid approach for the 0-1 multidimensional knapsack problem. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI, pp. 328-333 (2001)
176. Verhoeven, M.G.A., Aarts, E.H.L.: Parallel local search. Journal of Heuristics 1(1), 43-65 (1995)
177. Viśee, M., Teghem, J., Pirlot, M., Ulungu, E.L.: Two-phases method and branch and bound procedures to solve knapsack problem. Journal of Global Optimization 12, 139-155 (1998) (Pubitemid 128507670)
178. Voigt, H.-M., Born, J., Santibanez-Koref, I.: Modelling and Simulation of Distributed Evolutionary Search Processes for Function Optimization. In: Schwefel, H.-P.,M̈anner, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 373-380. Springer, Heidelberg (1991)
179. Voss, S.: Tabu search: Applications and prospects. In: Network Optimization Problems, pp. 333-353. World Scientific, USA (1993)
180. Wang, L.-H., Kao, C.-Y., Ouh-young, M., Chen, W.-C.: Molecular binding: A case study of the population-based annealing genetic algorithms. In: IEEE Int. Conf. on Evolutionary Computation, ICEC 1995, Perth, Australia, pp. 50-55 (December 1995)
181. Wright, A.H.: Genetic algorithms for real parameter optimization. In: Foundation of Genetic Algorithms, pp. 205-218. Morgan Kaufmann (1991)
182. Yagiura, M., Ibaraki, T.: Metaheuristics as robust and simple optimization tools. In: IEEE Int. Conf. on Evolutionary Computation, ICEC 1996, pp. 541-546 (1996)
183. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. on Evolutionary Computation 3(4), 257-271 (1999) (Pubitemid 30544879)