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Parallel genetic algorithms with schema migration

Authors: Yu Guan; Baowen Xu; Zhenqiang Chen; K.R.P.H. Leung;

Parallel genetic algorithms with schema migration

Abstract

Genetic algorithms (GAs) are efficient non-gradient stochastic search methods. Parallel GAs are proposed to overcome the deficiencies of sequential GAs, such as low speed and aptness to locally converge. However the tremendous communication cost incurred offsets the advantages of parallel GAs. Hence reducing communication cost is the key issue of this problem. Instead of reducing the communication cost simply by compressing the size of the messages, we tackle the problem by improving the effectiveness of the schema to be disseminated. We propose a new schema migration scheme (SMS). This SMS consists of a schema extracting mechanism and a schema disseminating mechanism. This SMS is valid and requires less communication cost.

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    popularity
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    influence
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Found an issue? Give us feedback
citations
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!
7
Average
Top 10%
Average
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