Cross-entropy and rare events for maximal cut and partition problems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue: Rare event simulation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations
Mathematical Programming: Series A and B
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimal mixing evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Linkage neighbors, optimal mixing and forced improvements in genetic algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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For an evolutionary algorithm (EA) to be efficiently scalable, variation must be linkage friendly. For this reason many EAs have been introduced that build and exploit linkage models, amongst which are estimation-of-distribution algorithms (EDAs). Although various models have been empirically evaluated, it remains of key importance to better understand the conditions under which model building is successful. In this paper, we consider the linkage tree genetic algorithm (LTGA). LTGA is a recent powerful linkage-learning EA that builds a hierarchical linkage model known as the linkage tree (LT). LTGA exploits this model using an intensive mixing procedure aimed at optimally exchanging building blocks. Empirical evaluation studies of LTGA have appeared in literature using different entropy-based measures for building the LT, but with comparable results. We study the differences in these measures to better understand the requirements for detecting important linkage information and point out why some measures are more successful than others.