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Title: A conceptual modeling of meme complexes in stochastic search
Authors: Chen, Xianshun
Ong, Yew Soon
Keywords: DRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity
Issue Date: 2012
Source: Chen, X. S., & Ong, Y. S. (2012). A conceptual modeling of meme complexes in stochastic search. IEEE transactions on systems, man, and cybernetics, part c (applications and reviews), 42(5), 612-625.
Series/Report no.: IEEE transactions on systems, man, and cybernetics, part c (applications and reviews)
Abstract: In science, gene provides the instruction for making proteins, while meme is the sociocultural equivalent of a gene containing instructions for carrying out behavior. Taking inspiration from nature, we model the memeplex in search as instructions that specify the coadapted meme complexes of individuals in their lifetime. In particular, this paper presents a study on the conceptual modeling of meme complexes or memeplexes for more effective problem solving in the context of modern stochastic optimization. The memeplex representation, credit assignment criteria for meme coadaptation, and the role of emergent memeplexes in the lifetime learning process of a memetic algorithm in search are presented. A coadapted memetic algorithm that takes the proposed conceptual modeling of memeplexes into actions to solve capacitated vehicle routing problems (CVRPs) of diverse characteristics is then designed. Results showed that adaptive memeplexes provide a means of creating highly robust, self-configuring, and scalable algorithms, thus generating improved or competitive results when benchmarking against several existing adaptive or human-designed state-of-the-art memetic algorithms and metaheuristics, on a plethora of CVRP sets considered.
DOI: 10.1109/TSMCC.2012.2188832
Fulltext Permission: none
Fulltext Availability: No Fulltext
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