Initial performance comparisons for the delta coding algorithm

Author

Mathias, Keith E. ; Whitley, L. Darrell

Author_Institution

Dept. of Comput. Sci., Colorado State Univ., Fort Collins, CO, USA

fYear

1994

fDate

27-29 Jun 1994

Firstpage

433

Abstract

Delta coding is an iterative genetic search strategy that sustains search by periodically re-initializing the population. This helps to avoid premature convergence during genetic search. Delta coding also remaps hyperspace with each iteration in an attempt to locate “easier” search spaces with respect to genetic search. Here, the optimization ability of delta coding is compared against the CHC genetic algorithm and a mutation driven stochastic hill-climbing algorithm on a suite of standard genetic algorithm test functions

Keywords

encoding; genetic algorithms; iterative methods; search problems; stochastic processes; CHC genetic algorithm; delta coding algorithm; hyperspace; initial performance comparisons; iterative genetic search strategy; mutation driven stochastic hill-climbing algorithm; optimization ability; periodic re-initialization; population; search spaces; standard genetic algorithm test functions; Computer science; Convergence; Decoding; Genetic algorithms; Genetic mutations; Hypercubes; Iterative algorithms; Sampling methods; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.349911

Filename

349911