Abstract
Using unconstrained binary quadratic programming problem as a case study, we investigate the role of multi-parent crossover operators within the memetic algorithm framework. We evaluate the performance of four multi-parent crossover operators (called MSX, Diagonal, U-Scan and OB-Scan) and provide evidences and insights as to why one particular multi-parent crossover operator leads to better computational results than another one. For this purpose, we employ several indicators like population entropy and average solution distance in the population.
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References
Moscato, P.: Memetic algorithms: a short introduction. In: New Ideas in Optimization, pp. 219–234. Mcgraw-Hill Ltd., Maidenhead (1999)
Kochenberger, G.A., Glover, F., Alidaee, B., Rego, C.: A unified modeling and solution framework for combinatorial optimization problems. OR Spectrum 26, 237–250 (2004)
Borgulya, I.: An evolutionary algorithm for the binary quadratic problems. Advances in Soft Computing 2, 3–16 (2005)
Katayama, K., Tani, M., Narihisa, H.: Solving large binary quadratic programming problems by an effective genetic local search algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 643–650. Morgan Kaufmann, San Francisco (2000)
Lodi, A., Allemand, K., Liebling, T.M.: An evolutionary heuristic for quadratic 0-1 programming. European Journal of Operational Research 119(3), 662–670 (1999)
Merz, P., Katayama, K.: Memetic algorithms for the unconstrained binary quadratic programming problem. BioSystems 78, 99–118 (2004)
Lü, Z., Hao, J.K., Glover, F.: A study of memetic search with multi-parent crossover for UBQP. In: Cowling, P., Merz, P. (eds.) EvoCOP 2010. LNCS, vol. 6022, pp. 154–165. Springer, Heidelberg (2010)
Glover, F., Kochenberger, G.A., Alidaee, B.: Adaptive memory tabu search for binary quadratic programs. Management Science 44, 336–345 (1998)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Boston (1997)
Eiben, A.E., Raué, P.E., Ruttkay, Z.: Genetic algorithms with multi-parent recombination. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 78–87. Springer, Heidelberg (1994)
Ting, C.K.: Design and analysis of multi-parent genetic algorithms, PhD Thesis, Univerity of Paderborn (2005)
Palubeckis, G.: Multistart tabu search strategies for the unconstrained binary quadratic optimization problem. Annals of Operations Research 131, 259–282 (2004)
Fleurent, C., Ferland, J.A.: Object-oriented implementation of heuristic search methods for graph coloring, maximum clique, and satisfiability. In: Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 26, pp. 619–652 (1996)
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Wang, Y., Lü, Z., Hao, JK. (2010). A Study of Multi-parent Crossover Operators in a Memetic Algorithm. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_56
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DOI: https://doi.org/10.1007/978-3-642-15844-5_56
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