The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy
SAC98: 13th Annual ACM Symposium on Applied Computing, Atlanta, Georgia (USA), February 1998, pp. 349-355
This paper proposes a new general approach for optimization algorithms in the Evolutionary Computation field. The approach is inspired by the Selfish Gene theory, an interpretation of the Darwinian theory given by the biologist Richard Dawkins, in which the basic element of evolution is the gene, rather than the individual. The paper defines the Selfish Gene Algorithm, that implements such a view of the evolution mechanism. We tested the approach by implementing a Selfish Gene Algorithm on a case study, and we found better results than those provided by a Genetic Algorithm on the same problem and with the same fitness function.
|sac98.pdf||Adobe Acrobat portable document|
|sac98.ps.gz||postscript document, compressed (with gzip)|
Copyright note for papers published by ACM: Permission to make digital or hard copies of this work for personal or classroom use is granted without fee provided that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to distribute to lists, requires prior specific permission and/or a fee.
[CSSq98] F. Corno, M. Sonza Reorda, G. Squillero, "The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy," SAC98: 13th Annual ACM Symposium on Applied Computing, Atlanta, Georgia (USA), February 1998, pp. 349-355