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BioData Min. 2018 Feb 17;11:2. doi: 10.1186/s13040-018-0164-x. eCollection 2018.

Investigating the parameter space of evolutionary algorithms.

Author information

1Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, 19104-6021 PA USA.
2Department of Computer Science, Ben-Gurion University, Beer Sheva, 8410501 Israel.


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.


Evolutionary algorithms; Genetic programming; Hyper-parameter; Meta-genetic algorithm; Parameter tuning

Conflict of interest statement

Not applicable.Not applicable.None of the authors have competing interests in this manuscript.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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