Predicting solution rank to improve performance

Michael D. Schmidt, Hod Lipson
2010 Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10  
Many applications of evolutionary algorithms utilize fitness approximations, for example coarse-grained simulations in lieu of computationally intensive simulations. Here, we propose that it is better to learn approximations that accurately predict the ranks of individuals rather than explicitly estimating their real-valued fitness values. We present an algorithm that coevolves a rankpredictor which optimizes to accurately rank the evolving solution population. We compare this method with a
more » ... lar algorithm that uses fitness-predictors to approximate real-valued fitnesses. We benchmark the two approaches using thousands of randomly-generated test problems in Symbolic Regression with varying difficulties. The rank prediction method showed a 5-fold reduction in computational effort for similar convergence rates. Rank prediction also produced less bloated solutions than fitness prediction.
doi:10.1145/1830483.1830652 dblp:conf/gecco/SchmidtL10a fatcat:hn7xluj7t5c6xc5yvfcvtmcexy