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A many-objective test problem for visually examining diversity maintenance behavior in a decision space

Published: 12 July 2011 Publication History

Abstract

Recently distance minimization problems in a two-dimensional decision space have been utilized as many-objective test problems to visually examine the behavior of evolutionary multi-objective optimization (EMO) algorithms. Such a test problem is usually defined by a single polygon where the distance from a solution to each vertex is minimized in the decision space. We can easily generate different test problems from different polygons. We can also easily generate test problems with multiple equivalent Pareto optimal regions using multiple polygons of the same shape and the same size. Whereas these test problems have a number of advantages, they have no clear relevance to real-world situations since they are artificially generated unrealistic test problems. In this paper, we generate a distance minimization problem from a real-world map. Our test problem has four objectives, which are to minimize the distances to the nearest elementary school, junior high school, railway station, and convenience store. Using our test problem, we examine the behavior of well-known and frequently-used EMO algorithms in terms of their diversity maintenance ability in the two-dimensional decision space.

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    cover image ACM Conferences
    GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
    July 2011
    2140 pages
    ISBN:9781450305570
    DOI:10.1145/2001576
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    Published: 12 July 2011

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    Author Tags

    1. decision space diversity
    2. evolutionary multiobjective optimization (EMO)
    3. many-objective optimization problems

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