Investigation of new operators for a diploid genetic algorithm

Sima Etaner Uyar; A. Emre Harmanci

doi:10.1117/12.367702

1 November 1999 Investigation of new operators for a diploid genetic algorithm

Sima Etaner Uyar, A. Emre Harmanci

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367702
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

This study involves diploid genetic algorithms in which a diploid representation of individuals is used. This type of representation allows characteristics that may not be visible in the current population to the preserved in the structure of the individuals and then be expressed in a later generation. Thus it prevents traits that may be useful from being lost. It also helps add diversity to the genetic pool of the population. In conformance with the diploid representation of individuals, a reproductive scheme which models the meiotic cell division for gamete formation in diploid organisms in nature is employed. A domination strategy is applied for mapping an individual's genotype onto its phenotype. The domination factor of each allele at each location is determined by way of a statistical scan of the population in the previous generation. Classical operators such as cross-over and mutation are also used in the new reproductive routine. The next generation of individuals are chosen via a fitness proportional method from among the parents and the offspring combined. To prevent early convergence and the population overtake of certain individuals over generations, an age counter is added. The effectiveness of this algorithm is shown by comparing it with the simple genetic algorithm using various test functions.

Citation Download Citation

Sima Etaner Uyar and A. Emre Harmanci "Investigation of new operators for a diploid genetic algorithm", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367702

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