Steady-state ALPS for real-valued problems
Gregory S. Hornby · 2009
The objectives of this paper are to describe a steady-state version of theAge-LayeredPopulationStructure(ALPS)EvolutionaryAlgorithm (EA) and to compare it against other GAs on real-valued problems. Motivation for this work comes from our previous success in demonstrating that a generational version of ALPS greatly improves searchperformance onaGenetic Programmingproblem. Inmakingsteady-stateALPS,somemodificationsweremadetothe method for calculating age and the method for moving individuals upagelayers. TodemonstratethatALPSworkswellonreal-valued problems we compare it against CMA-ES and Differential Evolution (DE) on five challenging, real-valued functions and on one real-worldproblem. WhileCMA-ESandDEoutperform ALPSon the two unimodal test functions, ALPS is much better on the three multimodal test problems and on the real-world problem. Further examination shows that, unlike the other GAs, ALPS maintains a genotypically diverse population throughout the entire search process. These findings strongly suggest that the ALPS paradigm is better able toavoidpremature convergence then the other GAs. Categories andSubject Descriptors