The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Spurious dependencies and EDA scalability
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Interaction detection by NFE estimation: a practical view of building blocks
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Model accuracy in the Bayesian optimization algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Substructural neighborhoods for local search in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Sub-structural niching in non-stationary environments
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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This paper proposes a niching scheme, the dependency structure matrix restricted tournament replacement (DSMRTR). The restricted tournament replacement (RTR) is a well-known niching scheme in the field of estimation of distribution algorithms (EDAs). However, RTR induces spurious dependencies among variables, which impair the performance of EDAs. This paper utilizes building-block-wise distances to define a new distance metric, the one-niche distance. For those EDAs which provide explicit linkage information, the one-niche distances can be directly incorporated into RTR. For EDAs without such information, DSMRTR constructs a dependency structure matrix via the differential mutual complement to estimate the one-niche distances. Empirical results show that DSMRTR induces fewer spurious dependencies than RTR does while maintaining enough diversity for EDAs.