Marc Richards’s research while affiliated with Colorado State University and other places

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Publications (1)


Figure 1: GP individuals represented as parse trees
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP
  • Article
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July 2006

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40 Reads

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5 Citations

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Marc Richards

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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.

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Citations (1)


... In this way, a robust individual, once on the slopes of the high narrow peak, will be less likely to produce offspring prone to " falling off " in the event of a small change to its structure. Another investigation by Whitley et al. [23] compared steady-state with generational replacement strategies using tournament sizes of 2 and 7 on several popular GP problems including Artificial Ant, 11 Multiplexer and a symbolic regression problem. The results of that study showed that a generational strategy with tournament size of 2 was the worst performing whereas the steady-state strategy with tournament size of 2 was best overall. ...

Reference:

Selection Bias and Generalisation Error in Genetic Programming.
Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP