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Learning and Lineage Selection in Genetic Algorithms

Authors: G.W. Braught;

Learning and Lineage Selection in Genetic Algorithms

Abstract

Lineage selection is a process by which traits that are not directly assessed by the fitness function can evolve. Reported here is an investigation of the effects of individual learning on the evolution of one such trait, self-adaptive mutation rates. It is found that the efficacy of the learning mechanism employed (its potential to increase individual fitness) has a significant effect on the number of generations required for self-adaptive mutation rates to evolve. When highly efficient learning mechanisms are used the evolution of self-adaptive mutation rates requires a greater number of generations than in the absence of learning. Conversely, when less efficient learning mechanisms are used fewer generations are required, as compared to the non-learning case.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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