The performance of polyploid evolutionary algorithms is improved both by having many chromosomes and by having many copies of each chromosome on symbolic regression problems

Author

Cavill, Rachel ; Smith, Steve ; Terrell, A.

Author_Institution

Dept. of Electron., Univ. of York

Volume

1

fYear

2005

fDate

5-5 Sept. 2005

Firstpage

935

Abstract

This paper presents important new findings for a new method for evolving individual programs with multiple chromosomes. Previous results have shown that evolving individuals with multiple chromosomes produced improved results over evolving individuals with a single chromosome. The multiple chromosomes are organised along two axes; there are a number of different chromosomes and a number of copies of each chromosome. This paper investigates the effects which these two axes have on the performance of the algorithm; whether the improvement in performance comes from just one of these features or whether it is a combination of them both

Keywords

biology; cellular biophysics; evolutionary computation; regression analysis; multiple chromosomes; polyploid evolutionary algorithm; symbolic regression problem; Biological cells; Biological control systems; Event detection; Evolutionary computation; Genetic algorithms; Genetic programming; History; Humans; Microorganisms; Organisms;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2005. The 2005 IEEE Congress on

Conference_Location

Edinburgh, Scotland

Print_ISBN

0-7803-9363-5

Type

conf

DOI

10.1109/CEC.2005.1554783

Filename

1554783