The simplex method and evolutionary algorithms

Author

Smith, Stephen

Author_Institution

Central Queensland Univ., Rockhampton, Qld., Australia

fYear

1998

fDate

4-9 May 1998

Firstpage

799

Lastpage

804

Abstract

Nelder and Mead´s (1965) simplex algorithm has often been used in many evolutionary algorithms as a `local hill-climber´ to try and improve the rate of convergence. Typically, every so often, a simplex is formed about a single (good) individual and then the simplex algorithm is run separately from the generational system. In this paper, we show that by defining each vertex of the simplex to be an individual selected from the population, and `evolving´ it for a small number of function evaluations, a stable genetic operator is produced, which can used in conjunction with other genetic operators. We also prove that this genetic operator can be coded in such away that the operator will not fail, computationally, regardless of the choice of vertices

Keywords

convergence; function evaluation; genetic algorithms; mathematical operators; search problems; computational fault tolerance; convergence rate; direct search technique; evolutionary algorithms; function evaluations; generational system; local hill-climber; simplex algorithm; simplex vertices; stable genetic operator; Biological cells; Convergence; Evolutionary computation; Genetics; Mathematical analysis; Optimization methods; Programming profession;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

Print_ISBN

0-7803-4869-9

Type

conf

DOI

10.1109/ICEC.1998.700154

Filename

700154