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Most problems studied in artificial intelligence possess some form of structure, but a precise way to define such structure is so far lacking. We investigate how the notion of problem structure can be made precise, and propose a formal definition of problem structure. The definition is applicable to problems in which the quality of candidate solutions is(More)
In many problems of interest, performance can be evaluated using tests, such as examples in concept learning, test points in function approximation, and opponents in game-playing. Evaluation on all tests is often infeasible. Identification of an accurate evaluation or fitness function is a difficult problem in itself, and approximations are likely to(More)
Coevolution can in principle provide progress for problems where no accurate evaluation function is available. An important open question however is how coevolution can be set up such that progress can be ensured. Previous work has provided progress guarantees either for limited cases or using strict acceptance conditions that can result in stalling. We(More)
In this paper we present a new Estimation–of–Distribution Algorithm (EDA) for Genetic Programming (GP). We propose a probability distribution for the space of trees, based on a grammar. To introduce dependencies into the distribution, grammar transformations are performed that facilitate the description of specific subfunctions. We present some results from(More)
Current methods for evolutionary computation can reliably address problems for which the dependencies between variables are limited to a small order k. Furthermore, several recent methods can address certain hierarchical problems which feature dependencies between all variables. In addition to modularity and hierarchy, a third problem feature that can be(More)
Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms,(More)
— This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic programming algorithm that only computes stationary policies. We observe that for guaranteeing convergence to the unique Pareto optimal set(More)
Genetic algorithms generally use a fixed problem representation that maps variables of the search space to variables of the problem, and operators of variation that are fixed over time. This limits their scal-ability on non-separable problems. To address this issue, methods have been proposed that coevolve explicitly represented modules. An open question is(More)
Acknowledgdements I would like to thank Edwin de Jong for many discussions and detailed comments on earlier drafts of this report. Part of the research presented here was done during a stay at the Theory and Formal Methods section of Imperial College in London. I am grateful to Chris Hankin of the ESPRIT COORDINATION-project for making this possible and for(More)