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Genetic Algorithms for Changing Environments (1992)  (Make Corrections)  (29 citations)
John Grefenstette
Parallel Problem Solving from Nature 2 (Proc. 2nd Int. Conf. on Parallel Problem Solving from Nature, Brussels 1992)



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Abstract: Genetic algorithms perform an adaptive search by maintaining a population of candidate solutions that are allocated dynamically to promising regions of the search space. The distributed nature of the genetic search provides a natural source of power for searching in changing environments. As long as sufficient diversity remains in the population the genetic algorithm can respond to a changing response surface by reallocating future trials. However, the tendency of genetic algorithms to converge ... (Update)

Cited by:   More
Degree of Population Diversity - A Perspective on Premature.. - Leung, Gao, Xu (1997)   (Correct)
From Optimization to Learning in Changing Environments: The .. - Gaspar, Hirsbrunner (2002)   (Correct)
Memory Enhanced Evolutionary Algorithms for Changing Optimization .. - Branke (1999)   (Correct)

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0.3:   Genetic Algorithms and Machine Learning - Grefenstette (1993)   (Correct)
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Related documents from co-citation:   More   All
11:   Genetic Algorithms for Tracking Changing Environments (context) - Cobb, Grefenstette - 1993
10:   Adaptation in Natural and Artificial Systems (context) - Holland - 1975
8:   Nonstationary function optimization using genetic algorithms with dominance and .. (context) - Goldberg, Smith - 1987

BibTeX entry:   (Update)

John J. Grefenstette. Genetic Algorithms for changing environments. In Proceedings of Parallel Problem Solving From Nature (PPSN-2), Brussels, 28-30 September, pages 137--144, 1992. http://citeseer.ist.psu.edu/grefenstette92genetic.html   More

@inproceedings{ grefenstette92genetic,
    author = "John J. Grefenstette",
    title = "Genetic algorithms for changing environments",
    booktitle = "Parallel {P}roblem {S}olving from {N}ature 2 ({P}roc. 2nd {I}nt. {C}onf. on {P}arallel {P}roblem {S}olving from {N}ature, {B}russels 1992)",
    publisher = "Elsevier",
    address = "Amsterdam",
    editor = "R. M{\"a}nner and B. Manderick",
    pages = "137--144",
    year = "1992",
    url = "citeseer.ist.psu.edu/grefenstette92genetic.html" }
Citations (may not include all citations):
1931   Adaptation in natural and artificial systems (context) - Holland - 1975
154   Optimization of control parameters for genetic algorithms (context) - Grefenstette - 1986
79   Learning sequential decision rules using simulation models a.. - Grefenstette, Ramsey et al. - 1990
18   An investigation into the use of hypermutation as an adaptiv.. (context) - Cobb - 1990
15   Micro-genetic algorithms for stationary and non-stationary f.. (context) - Krishnakumar - 1989
12   An analysis of genetic based pattern tracking and cognitive-.. (context) - Pettit, Swigger - 1983
1   An analysis of the behavior of a class of genetic adaptive s.. (context) - Report - 1975
1   Nonstationary function optimization using genetic dominance .. (context) - dissertation, Michigan et al. - 1987
1   Evolution in time and space: The parallel genetic algorithm (context) - Muhlenbien - 1990



The graph only includes citing articles where the year of publication is known.


Documents on the same site (http://sun15.aic.nrl.navy.mil/~gref/papers.html):   More
Predictive Models Using Fitness Distributions of Genetic.. - Grefenstette (1995)   (Correct)
Lamarckian Learning in Multi-agent Environments - Grefenstette (1991)   (Correct)
Case-Based Anytime Learning - Ramsey, Grefenstette (1994)   (Correct)

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