Constructing dynamic test environments for genetic algorithms based on problem difficulty
In recent years the study of dynamic optimization problems has attracted an increasing interest from the community of genetic algorithms and researchers have developed a variety of approaches into genetic algorithms to solve these problems. In order to compare their performance, an important issue is the construction of standardized dynamic test environments. Based on the concept of problem difficulty, This work proposes a new dynamic environment generator using a decomposable trap function. With this generator, it is possible to systematically construct dynamic environments with changing and bounding difficulty and hence, we can test different genetic algorithms under dynamic environments with changing but controllable difficulty levels.
Citation : Yang, S. (2004) Constructing dynamic test environments for genetic algorithms based on problem difficulty. Proceedings of the 2004 IEEE Congress on Evolutionary Computation, 2, pp. 1262-1269
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes