Abstract
The performance of Genetic Algorithms hinges on the choice of “eligible parents” for carrying the genetic information from one generation to the next. Several methods have been proposed to identify the most promising mating partners for the continuation of the progeny.
We propose, in this paper, a measure of dissimilarity between individuals to be considered along with their actual fitnesses. This would help the emergence of more combinations of chromosomes within the population so that at least a few are better. The more is the dissimilarity between the parents, the better are the chances of producing fit children. After the problem introduction, we draw an analogy from biology to illustrate that the method should really work, then proceed with the implementation details and discuss the results.
Apparently the philosophy of this paper contradicts some of the views held in connection with niche and speciation, where breeding within the community is preferred. However the issues involved are different and this aspect is dealt with in detail elsewhere in the paper.
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© 1998 Springer-Verlag
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Rao, G.R., Gowdaz, K.C. (1998). A new dissimilarity measure to improve the GA performance. In: Mira, J., del Pobil, A.P., Ali, M. (eds) Methodology and Tools in Knowledge-Based Systems. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64582-9_779
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DOI: https://doi.org/10.1007/3-540-64582-9_779
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