Direction matters in high-dimensional optimisation

Cara MacNish, Xin Yao
2008 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)  
Directional biases are evident in many benchmarking problems for real-valued global optimisation, as well as many of the evolutionary and allied algorithms that have been proposed for solving them. It has been shown that directional biases make some kinds of problems easier to solve for similarly biased algorithms, which can give a misleading view of algorithm performance. In this paper we study the effects of directional bias for highdimensional optimisation problems. We show that the impact
more » ... directional bias is magnified as dimension increases, and can in some cases lead to differences in performance of many orders of magnitude. We present a new version of the classical evolutionary programming algorithm, which we call unbiased evolutionary programming (UEP), and show that it has markedly improved performance for high-dimensional optimisation.
doi:10.1109/cec.2008.4631115 dblp:conf/cec/MacNishY08 fatcat:5hc4att52bgbxdqobcwdzaqepu