(Enter summary)
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" }
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Genetic and evolutionary algorithms come of age (context) - Goldberg
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