This paper proposes the use of haploid gene dominance in genetic programming

Clack, C

Vekaria, K

English

UCL Discovery

Genetic Programming With Gene DominanceKanta Vekaria and Chris ClackDepartment of Computer ScienceUniversity College LondonGower StreetLondon WC1E 6BTUnited KingdomEmail: K.Vekaria@cs.ucl.ac.uk, C.Clack@cs.ucl.ac.ukABSTRACTThis paper proposes the use of haploidgene dominance in geneticprogramming.1. IntroductionDawkins’s model of evolution is based on the gene. Hepresents his theory of the gene as the fundamental unit ofnatural selection [Dawk89]. Genetic material in complexorganisms is represented using a diploid chromosome. Inthe diploid form a genotype carries one or more pairs ofchromosomes, each containing information for the samefunctions. The genes contained in one set can be regardedas a direct alternative to the genes in the other set. Whenbuilding the body the genes from one set compete withthose in the other set. Genes which are expressed in thephenotype of an organism are dominant and those thatdon’t are recessive. Some genes that have been known tobe dominant have become recessive in successivegenerations and vice versa. These dominancecharacteristics have evolved over generations.Genetic programming [Koza92] traditionally uses ahaploid chromosome. The haploid form contains all theinformation relevant to the problem. The genes do not haveassociated dominance values.2. GP with Dominance and HaploidyAssuming a correlation between program fitness andsubtree fitness, we propose an alternative method ofcrossover which uses a dominance operator.The parse tree contains the normal defined function andterminal sets. Each use of a function or terminal will havean associated dominance value. This dominance value willreflect how good each one is with respect to the fitness ofthe entire program.During crossover two parent trees are selected and theposition for crossover is selected at random. Once thesubtrees are chosen, the nodes from each subtree arecompared, from top to bottom and left to right. The nodewith greater dominance is used to create a new subtree.This is a recursive process. In the case where one tree isgreater than the other the remaining component in thelarger tree is simply copied to the new subtree. This newsubtree is then attached to the trees of the parent wherecrossover occurred. Figure 1 shows an example ofcrossover.Parent 1          Parent 2                  Child 1              Child 2   a                        z                            a                          z  /  \                      / \                         /  \                         /  \  b   c                 y      x                      b   y                     y     x       / \               / \    / \                          / \                    / \    / \      d   e           w  v  u  t                        d   e               d  e   u  t     / \                / \                                  / \                  / \     f  g             s   r                              s    r                s   r(c) and (y) are chosen for crossover(y) has a greater dominance value than (c)(d) has a greater dominance than (w)(e) has a greater dominance than (v)(s), (r) are dominant over (f), (g)Figure 1 Crossover using dominanceThe fitness is then evaluated and the dominance valueof the new subtree that was attached is increasedproportionately to the increase in fitness.On initialisation of the population, random dominancevalues are assigned.Preliminary experiments will use simple crossover notthe merge crossover shown in figure 1. Here dominance iscompared only at the top nodes of the chosen subtree. Thenode which is recessive is replaced by the dominant one.3. ProspectsWe will investigate whether, as in nature, this methodenhances the GP evolutionary process.  We will furtherinvestigate:        - the best way to increase dominance values relative          to program fitness.        - implications for evolutionary convergence.        - the application of dominance to ADFs and other          modular techniques.Bibliography[Dawk89] Dawkins, R. (1989). The Selfish Gene - New Ed.Oxford University Press, Great Britain. [Koza92] Koza, J.R. (1992). Genetic Programming: On theProgramming of Computers by Means of NaturalSelection. Cambridge, MA:MIT Press.

Genetic Programming with Gene Dominance

https://discovery.ucl.ac.uk/10087126/1/gp97.pdf

Genetic Programming with Gene Dominance

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