The number of parameters that need to be man ually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algo rithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPS EA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually.

Meeting Name

2007 IEEE Congress on Evolutionary Computation (2007: Sep. 25-28, Singapore)


Computer Science

Keywords and Phrases

Evolutionary Algorithms; Greedy Population Sizing; Automated Population Size Tuning Method

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

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© 2007 Intitute of Electrical and Electronics Engineers, Inc., All rights reserved.

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