Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Evolutionary Computation
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
Efficient Linkage Discovery by Limited Probing
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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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(lk) function evaluations.