Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/26110

Title

An accelerated genetic algorithm

Author(s)

Podlena, John R.;  Hendtlass, Tim

Abstract

The standard Genetic Algorithm, originally inspired by natural evolution, has displayed its effectiveness in solving a wide variety of complex problems. This paper describes the use of the natural phenomenon known as the Baldwin effect (or cross-generational learning) as an enhancement to the standard Genetic Algorithm. This is implemented by using an artificial neural network to store aspects of the population's history. It also describes a method by which the negative side effects of a large elite sub-population can be counter-balanced by using an ageing coefficient in the fitness calculation.

Publication type

Journal article

Research centre

Swinburne University of Technology. School of Biophysical Sciences and Electrical Engineering. Centre for Intelligent Systems

Source

Applied Intelligence, Vol. 8, no. 2 (March 1998), p. 103-111

Publication year

1998

Keyword(s)

Accelerated genetic algorithms;  Baldwin effect;  Efficiency;  Environmental impact;  History;  Neural networks;  Optimisation;  Population statistics;  Standards

Publisher

Springer

ISSN

0924-669X

Publisher URL

http://dx.doi.org/10.1023/A:1008227606285

Peer reviewed