Restart scheduling for genetic algorithms
In order to escape from local optima, it is standard practice to periodically restart heuristic optimization algorithms such as genetic algorithm according to some restart criteria/policy. This paper addresses the issue of finding a good restart strategy in the context of resource-bounded optimization scenarios, in which the goal is to generate the best possible solution given a fixed amount of time. We propose the use of a restart scheduling strategy which generates a static restart strategy with optimal expected utility, based on a database of past performance of the algorithm on a class of problem instances. We show that the performance of static restart schedules generated by the approach can be competitive to that of a commonly used dynamic restart strategy based on detection of lack of progress.
KeywordsGenetic Algorithm Problem Instance Expected Utility Dynamic Strategy Objective Function Evaluation
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- 2.R.J. Collins and D.R. Jefferson. Selection in massively parallel genetic algorithms. In Proc. International Conf. on Genetic Algorithms (ICGA), pages 249–256, 1991.Google Scholar
- 3.Y. Davidor, T. Yamada, and R. Nakano. The ECOlogical Framework II: Improving GA performance at virtually zero cost. In Proc. International Conf. on Genetic Algorithms (ICGA), pages 171–176, 1993.Google Scholar
- 4.K. DeJong. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. PhD thesis, University of Michigan, Department of Computer and Communication Sciences, Ann Arbor, Michigan, 1975.Google Scholar
- 5.L.J. Eshelman and J.D. Schaffer. Preventing premature convergence in genetic algorithms by preventing incest. In Proc. International Conf. on Genetic Algorithms (ICGA), pages 115–122, 1991Google Scholar
- 6.D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.Google Scholar
- 8.M. Hulin. An optimal stop criterion for genetic algorithms: a Bayesian approach. In Proc. International Conf. on Genetic Algorithms (ICGA), pages 135–141, 1997.Google Scholar