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.
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 |
|---|---|
| Pages (from-to) | 435-443 |
| Journal | Evolutionary Computation |
| Volume | 15 |
| Issue number | 4 |
| Early online date | 19 Nov 2007 |
| DOIs | |
| Publication status | Published - 01 Dec 2007 |
Keywords
- black-box optimiazation
- evolutionary algorithm
- problem difficulty measure
- running time
- satisfiability problem