A Design Approach to Problem Difficulty

  • David E. Goldberg
Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 7)

Abstract

The last chapter motivated and expanded upon the notion of a building block to understand the raw material available for genetic search. This chapter builds on these ideas to understand the ways in which a GA might succeed or fail depending upon the type of problem it faces. Our approach is to design bounding adversarial problems that represent different dimensions of problem difficulty. Success in this endeavor will allow us to test different algorithms against a limited number of boundedly difficult problems in such a way that algorithm success against the particular problems ensures success against a large class of problems no harder than the test cases. This adversarial design method contrasts sharply with the common practices of using historical, randomly generated, or ad hoc test functions.

Keywords

Building Block Problem Difficulty Design Envelope Archetypal Model Trap Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • David E. Goldberg
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUSA

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