Deterministic Divide-and-Conquer Algorithm for Decomposable Black-Box Optimization Problems with Bounded Difficulty
There is a class of GA-hard problems for which classical genetic algorithms often fail to obtain optimal solutions. In this paper, we focus on a class of very typical GA-hard problems that we call decomposable black-box optimization problems (DBBOP). Different from random methods in GA literature, two “deterministic” divide-and-conquer algorithms DA1 and DA2 are proposed respectively for non-overlapping and overlapping DBBOP, in which there are no classical genetic operations and even no random operations. Given any DBBOP with dimension l and bounded order k, our algorithms can always reliably and accurately obtain the optimal solutions in deterministic way using O(l k ) function evaluations.
KeywordsGenetic Algorithm Time Complexity Evolutionary Computation Decomposition Algorithm Deterministic Algorithm
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