Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

Sparse experimental design: an effective an efficient way discovering better genetic algorithm structures

Stewardson, D.J. and Whitfield, R.I. and Hicks, C. and Pongcharoen, P. and Braiden, P. (2001) Sparse experimental design: an effective an efficient way discovering better genetic algorithm structures. In: 2nd European Conference on intelligent Management Systems in Operations, 2001-07-03 - 2001-07-04.

[img]
Preview
PDF (strathprints006388.pdf)
strathprints006388.pdf

Download (152kB) | Preview

Abstract

The focus of this paper is the demonstration that sparse experimental design is a useful strategy for developing Genetic Algorithms. It is increasingly apparent from a number of reports and papers within a variety of different problem domains that the 'best' structure for a GA may be dependent upon the application. The GA structure is defined as both the types of operators and the parameters settings used during operation. The differences observed may be linked to the nature of the problem, the type of fitness function, or the depth or breadth of the problem under investigation. This paper demonstrates that advanced experimental design may be adopted to increase the understanding of the relationships between the GA structure and the problem domain, facilitating the selection of improved structures with a minimum of effort.