Abstract
In this paper, we introduce the ICE framework in which crossover from genetic algorithms (GAs) is incorporated in iterated density estimation evolutionary algorithms (IDEAs). We focus on permutation optimization problems and show how pure continuous IDEAs can be applied using the random keys representation. The problems that are hereby encountered,
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