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Title:Improving Evolutionary Algorithms with Scouting: High-Dimensional Problems
Authors: Konstantinos Bousmalis ; Jeffrey Pfaffmann ; Gillian Hayes
Date:Jun 2008
Publication Title:International Conference on Artificial Intelligence and Soft Computing, ICAISC 2008
Publication Type:Conference Paper Publication Status:Published
Volume No:5097/2008 Page Nos:365-375
DOI:10.1007/978-3-540-69731-2_36 ISBN/ISSN:978-3-540-69572-1
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 and discover solutions of higher fitness. The merit of the SEAs has been established in previous work with a number of two and three-dimensional test cases and a variety of configurations. In this paper, we will present two approaches to using SEAs to solve high dimensional problems. The first one involves the use of Locality Sensitive Hashing (LSH) for the repository of individuals, whereas the second approach entails the use of scouting driven mutation at a certain rate, the Scouting Rate. We will show that an SEA significantly improves the equivalent simple EA configuration with higher dimensional problems in an expeditious manner.
2008 by The University of Edinburgh. All Rights Reserved
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Bibtex format
author = { Konstantinos Bousmalis and Jeffrey Pfaffmann and Gillian Hayes },
title = {Improving Evolutionary Algorithms with Scouting: High-Dimensional Problems},
book title = {International Conference on Artificial Intelligence and Soft Computing, ICAISC 2008},
publisher = {Springer-Verlag},
year = 2008,
month = {Jun},
volume = {5097/2008},
pages = {365-375},
doi = {10.1007/978-3-540-69731-2_36},
url = {},

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