Investigating the role of diploidy in simulated populations of evolving individuals


Raffaele Calabretta, Riccardo Galbiati, Stefano Nolfi, Domenico Parisi

Abstract. In most work applying genetic algorithms to populations of neural networks there is no real distinction between genotype and phenotype. In nature both the information contained in the genotype and the mapping of the genetic information into the phenotype are usually much more complex. The genotypes of many organisms exhibit diploidy, i.e., they include two copies of each gene: if the two copies are not identical in their sequences and therefore have a functional difference in their products (usually proteins), the expressed phenotypic feature is termed the dominant one, the other one recessive (not expressed). In this paper we review the literature on the use of diploidy and dominance operators in genetic algorithms; we present the new results we obtained with our own simulations in changing environments; finally, we discuss some results of our simulations that parallel biological findings.




Raffaele Calabretta1,3, Riccardo Galbiati2, Stefano Nolfi1 and Domenico Parisi1


1 Department of Neural Systems and Artificial Life

Institute of Psychology, National Research Council

Viale Marx 15, 00137 Rome, Italy



2 Department of Biology, University "Tor Vergata"

Via della Ricerca scientifica, 00133 Rome, Italy


3 Department of Ecology and Evolutionary Biology, Yale University

165 Prospect Street, 06511 New Haven, CT, U.S.A.