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Bibliography on Memetic algorithms

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Number of references:338Last update:May 3, 2002
Number of online publications:50Supported:yes
Most recent reference:October 2000 Info:Version 3.2

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Pablo Moscato <moscato @ densis . fee . unicamp . br> (email mangled to prevent spamming)
DENSIS - FEEC - UNICAMP (Universidade Estadual de Campinas)
Memetic Algorithms is a population-based approach for heuristic search in optimization problems. They have shown that they are orders of magnitude faster than traditional Genetic Algorithms for some problem domains. Basically, they combine local search heuristics with crossover operators. For this reason, some researchers have viewed them as Hybrid Genetic Algorithms. However, combinations with constructive heuristics or exact methods may also belong to this class of metaheuristics. Since they are most suitable for MIMD parallel computers and distributed computing systems (including heterogeneous systems) as those composed by networks of workstations, they have also received the dubious denomination of Parallel Genetic Algorithms. Other researchers known it as Genetic Local Search.
Memetic Algorithm Home Page

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Bibliographic Statistics

article(165), inproceedings(110), incollection(23), book(17), techreport(9), misc(8), mastersthesis(3), phdthesis(3)
author(338), title(338), year(332), pages(285), volume(189), journal(165), number(145), publisher(137), booktitle(133), address(78), abstract(76), editor(75), note(25), series(24), keywordsplus(13), institution(10), authorkeywords(9), month(9), isbn(6), school(6), organisation(5), type(5), keywords(4), key(3), editors(2), ref(2), url(2), abstract-url(1), addresses(1), advisor(1), annote(1), author_keywords(1), cited_references(1), class(1), document_type(1), howpublished(1), ids_number(1), info(1), issn(1), language(1), notes(1), publisher_address(1), scope(1)
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