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Volumn 3673 LNAI, Issue , 2005, Pages 66-77

A survey of problem difficulty in genetic programming

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CORRELATION METHODS; MATHEMATICAL PROGRAMMING;

EID: 33646128244     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11558590_7     Document Type: Conference Paper
Times cited : (4)

References (16)
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    • M. Clergue, P. Collard, M. Tomassini, and L. Vanneschi. Fitness distance correlation and problem difficulty for genetic programming. In et al. W. B. Langdon, editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO'02, pages 724-732, New York City, USA, 2002. Morgan Kaufmann, San Francisco, CA. Best Conference Paper Award Nomination.
    • Proceedings of the Genetic and Evolutionary Computation Conference, GECCO'02 , pp. 724-732
    • Clergue, M.1    Collard, P.2    Tomassini, M.3    Vanneschi, L.4
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    • W. B. Langdon and R. Poli. An analysis of the max problem in genetic programming. In J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference on Genetic Programming, pages 222-230, San Francisco, CA, 1997. Morgan Kaufmann.
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  • 8
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    • to appear
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    • L. Vanneschi, M. Tomassini, M. Clergue, and P. Collard. Difficulty of unimodal and multimodal landscapes in genetic programming. In Cantú-Paz, E., et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '03, LNCS, pages 1788-1799, Chicago, Illinois, USA, 2003. Springer-Verlag. Best Conference Paper Award Nomination.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.