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GECCO
2006
Springer

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

9 years 5 months ago
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
L. Darrell Whitley, Marc D. Richards, J. Ross Beve
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors L. Darrell Whitley, Marc D. Richards, J. Ross Beveridge, André da Motta Salles Barreto
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