Improving the efficiency of the extended compact genetic algorithm

Authors:
Thyago S.P.C. Duque;David E. Goldberg;Kumara Sastry
Affiliations:
Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana Champaign, Urbana, IL, USA;Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana Champaign, Urbana, IL, USA;Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana Champaign, Urbana, IL, USA
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

Evolutionary Algorithms are largely used search and optimization procedures that, when properly designed, can solve intractable problems in tractable polynomial time. Efficiency enhancements are used to turn them from tractable to practical. In this paper we show preliminary results of two efficiency enhancements proposed for the Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from O($n^3$) to O($n^2$), speeding up the algorithm by 1000 times on a 4096 bits problem. Then, local-search hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results draw the first steps toward a competent and efficient Genetic Algorithm.