Evolutionary Consequences of Coevolving Targets

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@Article{pagie97evolutionary,

• author = "Ludo Pagie and Paulien Hogeweg",
• title = "Evolutionary Consequences of Coevolving Targets",
• journal = "Evolutionary Computation",
• volume = "5",
• number = "4",
• pages = "401--418",
• year = "1997",
• month = "Winter",
• keywords = "genetic algorithms, genetic programming, coevolution, co-evolution, genotype-phenotype mapping, information integration, generalisability, mutational stability, onemax",
• URL = "http://www.santafe.edu/~ludo/articles/ecct.ps.gz",
• URL = "http://citeseer.ist.psu.edu/pagie98evolutionary.html",
• DOI = "doi:10.1162/evco.1997.5.4.401",
• size = "28 pages",
• abstract = "Most evolutionary optimisation models incorporate a fitness evaluation that is based on a predefined static set of test cases or problems. In the natural evolutionary process, selection is of course not based on a static fitness evaluation. Organisms do not have to combat every existing disease during their lifespan; organisms of one species may live in different or changing environments; different species coevolve. This leads to the question of how information is integrated over many generations.

  This study focuses on the effects of different fitness evaluation schemes on the types of genotypes and phenotypes that evolve. The evolutionary target is a simple numerical function. The genetic representation is in the form of a program (i.e., a functional representation, as in genetic programming). Many different programs can code for the same numerical function. In other words, there is a many-to-one mapping between genotype (the programs) and phenotypes. We compare fitness evaluation based on a large static set of problems and fitness evaluation based on small coevolving sets of problems. In the latter model very little information is presented to the evolving programs regarding the evolutionary target per evolutionary time step. In other words, the fitness evaluation is very sparse. Nevertheless the model produces correct solutions to the complete evolutionary target in about half of the simulations. The complete evaluation model, on the other hand, does not find correct solutions to the target in any of the simulations. More important, we find that sparse evaluated programs are better generalisable compared to the complete evaluated programs when they are evaluated on a much denser set of problems. In addition, the two evaluation schemes lead to programs that differ with respect to mutational stability; sparse evaluated programs are less stable than complete evaluated programs.",

• notes = "p6 'We embedded the populations of solutions and parasites in space. We used a 2-D toroidal square lattice...' p10 Solutions evolved without coevolution are more difficult to find and of a different form. p11 'The complete sampling results in severe selection against the occurrence of errors.' p11 'increased complexity reduces the generalisability of the solutions' p11 Mutational stability p14 'statically evaluated programs are more stable than the' coevolved programs. p15 adds 'much less generalizable'.

  p20 'the coevolutionary evaluation scheme works best if the population size is of the same order as the size of the complete set of problems.'

  p20 'coevolving evaluation scheme needs much larger populations... computational cost nevertheless favours the coevolutionary...' p20 'random evaluation scheme' p20 'In the 2-D model the coevolving evaluation scheme is not much more efficient than the random evaluation scheme.' but see following text. p21 Figure 7. p23 'Thus the sparseness of the evaluation helps (rather than hinders) the search.'",

}

Genetic Programming entries for Ludo Pagie Paulien Hogeweg

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