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Title:Improving Evolutionary Algorithms with Scouting
Authors: Konstantinos Bousmalis ; Gillian Hayes ; Jeffrey Pfaffmann
Date:Dec 2007
Publication Title:Proceedings of the 13th Portuguese Conference on Artificial Intelligence-EPIA 2007
Publication Type:Conference Paper Publication Status:Pre-print

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 Scouting-Inspired 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.

2007 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Konstantinos Bousmalis and Gillian Hayes and Jeffrey Pfaffmann },
title = {Improving Evolutionary Algorithms with Scouting},
book title = {Proceedings of the 13th Portuguese Conference on Artificial Intelligence-EPIA 2007},
publisher = {Springer},
year = 2007,
month = {Dec},

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