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Gene expression programming with multiple chromosomes

Authors: Bo Wang; Min Yao; Rong Zhu;

Gene expression programming with multiple chromosomes

Abstract

Gene expression programming (GEP) has been widely used in the areas of pattern recognition and knowledge discovery, however, when dealing with complicated problems, it is very time-consuming and the number of generations is large. In order to overcome these drawbacks, this paper proposes a multi-chromosomes GEP algorithm (MC-GEP). Firstly, the individual is composed of multiple chromosomes, each chromosome consists of one or more genes. Secondly, the expression of each chromosome or combinations of several chromosomes may be chosen to indicate the individual. Finally, chromosome recombination is changed and performed orderly like meiosis. Experimental results show that MC-GEP can reduce the running time and the number of generations with respect to the GEP.

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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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