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Improving Genetic Algorithms Performance via Deterministic Population Shrinkage

Juan Luis Jimenez Laredo, Carlos Fernandes, Juan Julian Merelo and Christian Gagné

Abstract - Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations ofspeed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.

Bibtex:

@inproceedings{and 774,
author    = { Juan Luis Jimenez Laredo and Carlos Fernandes and Juan Julian Merelo and Christian Gagné },
title     = { Improving Genetic Algorithms Performance via Deterministic Population Shrinkage },
booktitle = { Proc. of Genetic and Evolutionary Computation Conference (GECCO 2009) },
year      = { 2009 },
location  = { Montreal (Quebec), Canada }
}

Last modification: 2009/04/15 by cgagne

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