Improving Evolutionary Algorithms with Scouting
نویسندگان
چکیده
The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an EA will often become trapped in a suboptimal solution(premature convergence). The ScoutingInspired Evolutionary Algorithm(SEA) is a relatively new technique that avoids premature convergence by determining whether a subspace has been explored sufficiently, and, if so, directing the search towards other parts of the system. Previous work has only focused on EAs with point mutation operators and standard selection techniques. This paper examines the effect of scouting on EA configurations that, among others, use crossovers and the Fitness-Uniform Selection Scheme(FUSS), a selection method that was specifically designed as means to avoid premature convergence. We will experiment with a variety of problems and show that scouting significantly improves the performance of all EA configurations presented.
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Improving Evolutionary Algorithms with Scouting: High-Dimensional Problems
Evolutionary Algorithms (EAs) are common optimization techniques based on the concept of Darwinian evolution. During the search for the global optimum of a search space, a traditional EA will often become trapped in a local optimum. The Scouting-Inspired Evolutionary Algorithms (SEAs) are a recently–introduced family of EAs that use a cross–generational memory mechanism to overcome this problem...
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