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Abstract
Various methods have been defined to measure the hardness of a fitness function for evolutionary algorithms and other black-box heuristics. Examples include fitness landscape analysis, epistasis, fitness-distance correlations etc., all of which are relatively easy to describe. However, they do not always correctly specify the hardness of the function. Some measures are easy to implement, others are more intuitive and hard to formalize. This paper rigorously defines difficulty measures in black-box optimization and proposes a classification. Different types of realizations of such measures are studied, namely exact and approximate ones. For both types of realizations, it is proven that predictive versions that run in polynomial time in general do not exist unless certain complexity-theoretical assumptions are wrong.
Original language | English |
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Pages (from-to) | 435-443 |
Number of pages | 9 |
Journal | Evolutionary Computation |
Volume | 15 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Keywords
- satisfiability problem
- problem difficulty measure
- evolutionary algorithm
- running time
- black-box optimization
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Dive into the research topics of 'A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Realizations and Predictability'. Together they form a unique fingerprint.Projects
- 1 Finished
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Computational Complexity Analysis of Evoloutionary Algarithms.
Engineering & Physical Science Research Council
1/05/05 → 31/10/08
Project: Research Councils