Abstract

Investigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years. This trend reflects the fact that many real world problems are actually dynamic, which poses serious challenge to traditional genetic algorithms. Several approaches have been developed into genetic algorithms for dynamic optimization problems. Among these approches, random immigrants and memory schemes have shown to be beneficial in many dynamic problems. This paper proposes a hybrid memory and random immigrants scheme for genetic algorithms in dynamic environments. In the hybrid scheme, the best solution in memory is retrieved and acts as the base to create random immigrants to replace the worst individuals in the population. In this way, not only can diversity be maintained but it is done more efficiently to adapt the genetic algorithm to the changing environment. The experimental results based on a series of systematically constructed dynamic problems show that the proposed memorybased immigrants scheme efficiently improves the performance of genetic algorithms in dynamic environments.

Citations

130	Evolutionary Optimization in Dynamic Environments - Branke - 2002
109	Nonstationary function optimization using genetic algorithm with dominance and diploidy - Goldberg, Smith - 1987
97	Genetic algorithms for changing environments - Grefenstette - 1992
87	Genetic algorithms for tracking changing environments - Cobb, Grefenstette - 1993
85	Memory enhanced evolutionary algorithms for changing optimization problems - Branke - 1999
64	Nonstationary function optimization using the structured genetic algorithm - Dasgupta, McGregor - 1992
55	A multipopulation approach to dynamic optimization problems - Branke, Kaußler, et al. - 2000
55	Experimental study on population-based incremental learning algorithms for dynamic optimization problems - Yang, Yao - 2005
47	A new diploid scheme and dominance change mechanism for non-stationary function optimization - Ng, Wong - 1995
38	Non-stationary problem optimization using the primal-dual genetic algorithms - Yang - 2003
33	Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm - Mori, Nishikawa - 1997
30	Genetic algorithms for open shop scheduling and rescheduling - Xu, Louis - 1996
26	A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments - Vavak, Fogarty - 1996
23	Searching for optima in nonstationary environments - Trojanowski, Michalewicz
14	Dynamic memory model for non-stationary optimization - Bendtsen, Krink
6	Case-based initializtion of genetic algorithms - Ramsey, Greffenstette - 1993