TOBIAS-lib - The Compact Memetic Algorithm

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Bitte zitieren sie dieses Dokument als / Please cite this document using URN: urn:nbn:de:bsz:21-opus-9040 URL: http://w210.ub.uni-tuebingen.de/dbt/volltexte/2003/904/	Dieses Dokument wird bereit gestellt von This document is hosted by TOBIAS-lib

Merz, Peter
International Workshop on Memetic Algorithms <Chicago, 2003>
Genetic and Evolutionary Computation Conference (GECCO) <Chicago, 2003>

The Compact Memetic Algorithm

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Kurzfassung in englisch
Optimization by probabilistic modeling is a growing research field in
evolutionary computation. An example is the compact genetic algorithm (cGA),
in which the population of a genetic algorithm (GA) is represented as a
probability distribution over the set of solutions. Both cGA algorithm
and the order-one behavior of a simple GA with uniform crossover are operationally
equivalent. The cGA is much easier to implement and requires less memory.
In this paper, memetic algorithms (MAs) are investigated in which the
population is replaced by a probability vector analogously to the cGA. The
resulting compact memetic algorithms (cMAs) hence require less memory, are
easier to implement and require fewer parameters than other MAs. It is shown
that cMAs with and without additional recombination perform comparable to or
better than population-based MAs on a set of benchmark instances of the
unconstrained binary quadratic programming problem.

SWD-Schlagwörter: Memetischer Algorithmus

Freie Schlagwörter (englisch): Memetic Algorithms , Compact Genetic Algorithm , Binary Quadratic Programming

Institut: Bereich Sonstige / Externe

DDC-Sachgruppe: Informatik

Dokumentart: InProceedings (Aufsatz / Paper einer Konferenz etc.)

Sprache: englisch

Erstellungsjahr: 2003

Publikationsdatum: 02.09.2003

Kurzfassung in englisch Optimization by probabilistic modeling is a growing research field in evolutionary computation. An example is the compact genetic algorithm (cGA), in which the population of a genetic algorithm (GA) is represented as a probability distribution over the set of solutions. Both cGA algorithm and the order-one behavior of a simple GA with uniform crossover are operationally equivalent. The cGA is much easier to implement and requires less memory. In this paper, memetic algorithms (MAs) are investigated in which the population is replaced by a probability vector analogously to the cGA. The resulting compact memetic algorithms (cMAs) hence require less memory, are easier to implement and require fewer parameters than other MAs. It is shown that cMAs with and without additional recombination perform comparable to or better than population-based MAs on a set of benchmark instances of the unconstrained binary quadratic programming problem.
SWD-Schlagwörter:		Memetischer Algorithmus
Freie Schlagwörter (englisch):		Memetic Algorithms , Compact Genetic Algorithm , Binary Quadratic Programming
Institut:		Bereich Sonstige / Externe
DDC-Sachgruppe:		Informatik
Dokumentart:		InProceedings (Aufsatz / Paper einer Konferenz etc.)
Sprache:		englisch
Erstellungsjahr:		2003
Publikationsdatum:		02.09.2003


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