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Genetic Algorithm Difficulty and the Modality of Fitness Landscapes (1994)  (Make Corrections)  (27 citations)
Jeffrey Horn, David E. Goldberg
Foundations of Genetic Algorithms 3



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Abstract: We assume that the modality (i.e., number of local optima) of a fitness landscape is related to the difficulty of finding the best point on that landscape by evolutionary computation (e.g., hillclimbers and genetic algorithms (GAs)). We first examine the limits of modality by constructing a unimodal function and a maximally multimodal function. At such extremes our intuition breaks down. A fitness landscape consisting entirely of a single hill leading to the global optimum proves to be hard for ... (Update)

Context of citations to this paper:   More

...) of stable suboptimal attractors. Wilson [6] proposes a deceptive function which is straightforwardly optimized by a GA. Horn and Goldberg [7] construct an easy maximally multimodal function. Finally, long path problems have been designed to show that even unimodal functions...

.... Section 3 presents a generic method to construct fitness functions which follow a given fitness distance relation, with Horn s longpath [HG95] as the main example. The method is then used in Section 4 to construct a class of functions whose members share the same FD...

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BibTeX entry:   (Update)

J. Horn and D. E. Goldberg. Genetic algorithm difficulty and the modality of fitness landscapes. In L. D. Whitley, editor, Foundations of Genetic Algorithms, volume 3, San Mateo, CA, 1994. Morgan Kaufmann. (To appear). http://citeseer.ist.psu.edu/article/horn94genetic.html   More

@incollection{ horn95genetic,
    author = "Jefrrey Horn and David E. Goldberg",
    title = "Genetic Algorithm Difficulty and the Modality of Fitness Landscapes",
    booktitle = "Foundations of {G}enetic {A}lgorithms 3",
    publisher = "Morgan Kaufmann",
    address = "San Francisco, CA",
    editor = "L. Darrell Whitley and Michael D. Vose",
    pages = "243--269",
    year = "1995",
    url = "citeseer.ist.psu.edu/article/horn94genetic.html" }
Citations (may not include all citations):
2138   Genetic Algorithms in Search (context) - Goldberg
1931   Adaptation in natural and artificial systems (context) - Holland - 1992
177   Genetic algorithms (context) - Goldberg, Deb et al. - 1992
80   When will a genetic algorithm outperform a hill climbing - Mitchell, Holland - 1993
67   Genetic algorithms and Walsh functions: part (context) - Goldberg - 1989
61   Deception considered harmful - Grefenstette - 1992
61   Genetic algorithms and Walsh functions: part II (context) - Goldberg
52   Genetic and evolutionary algorithms come of age (context) - Goldberg
51   Genetic Algorithms as Function Optimizers (context) - Bethke - 1981
51   Fundamental principles of deception in genetic search - Whitley - 1991
35   Simple genetic algorithms and the minimal deceptive problem (context) - Goldberg - 1987
20   Construction of high-order deceptive functions using low-ord.. (context) - Goldberg - 1991
17   A survey of the theory of hypercube graphs (context) - Harary, Hayes - 1988
16   GA-easy does not imply steepest-ascent optimizable - Wilson - 1991
14   Multimodal deceptive functions (context) - Deb, Horn et al. - 1993
13   Parallel Problem Solving From Nature (context) - Maenner, Manderick - 1991
12   Simple analytical models for genetic algorithms for multimod.. - Mahfoud - 1993
8   Reverse hillclimbing (context) - Jones, Rawlins - 1993
3   and genetic algorithms (context) - Systems, Goldberg et al. - 1992
3   Analysis and design of a general GA deceptive problem (context) - Homaifar, Qi et al. - 1991
3   Surfaces of selective value revisited (context) - Wright - 1988
1   Difference-preserving codes (context) - Preparata - 1974
1   Making genetic algorithms fly: a lesson from the Wright brot.. (context) - Mathematics, Intelligence et al.



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