Edwin D. de Jong

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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 be used to adaptively choose the tests used for evaluating candidate solutions. A long-standing question is how this dynamic setup may be organized to yield reliable search methods. Reliability can only be considered in connection with a particular solution concept specifying what constitutes a solution. Recently, monotonic coevolution(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)
Competent Genetic Algorithms can efficiently address problems in which the linkage between variables is limited to a small order <i>k</i>. Problems with higher order dependencies can only be addressed efficiently if further problem properties exist that can be exploited. An important class of problems for which this occurs is that of hierarchical problems.(More)
Emergent Geometric Organization and Informative Dimensions in Coevolutionary Algorithms A dissertation presented to the Faculty of the Graduate School of Arts and Sciences of Brandeis University, Waltham, Massachusetts by Anthony Bucci Coevolutionary algorithms vary entities which can play two or more distinct, interacting roles, with the hope of producing(More)
Various multi--objective evolutionary algorithms (MOEAs) have obtained promising results on various numerical multi--objective optimization problems. The combination with gradient--based local search operators has however been limited to only a few studies. In the single--objective case it is known that the additional use of gradient information can be(More)
Coevolution offers adaptive methods for the selection of tests used to evaluate individuals, but the resulting evaluation can be unstable. Recently, general archive-based coevolution methods have become available for which monotonic progress can be guaranteed. The size of these archives may grow indefinitely however, thus limiting their application(More)