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Title: Investigating the parameter space of evolutionary algorithms.

Authors: Sipper, Moshe; Fu, Weixuan; Ahuja, Karuna; Moore, Jason H

Published In BioData Min, (2018)

Abstract: Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

PubMed ID: 29467825 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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