Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP

In contrast with the diverse array of genetic algorithms, the Genetic Programming (GP) paradigm is usually applied in a relatively uniform manner. Heuristics have developed over time as to which replacement strategies and selection methods are best. The question addressed in this paper is relatively simple: since there are so many variants of evolutionary algorithm, how well do some of the other well known forms of evolutionary algorithm perform when used to evolve programs trees using s-expressions as the representation? Our results suggest a wide range of evolutionary algorithms are all equally good at evolving programs, including the simplest evolution strategies. Categories and Subject Descriptors I.2.2 [Automatic Programming]: [Program Synthesis] General Terms Experimentation, Performance Keywords Genetic Programming, Steady-State Genetic Algorithms, Evolution Strategies

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: L. Darrell Whitley, Marc D. Richards, J. Ross Beveridge, André da Motta Salles Barreto
• Keywords: Evolutionary Algorithms, GECCO 2006, Genetic Algorithms, Genetic Programming, Optimization