A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Investigation of the Fitness Landscapes in Graph Bipartitioning: An Empirical Study
Journal of Heuristics
Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning
Evolutionary Computation
Introduction to the Design and Analysis of Algorithms (2nd Edition)
Introduction to the Design and Analysis of Algorithms (2nd Edition)
Towards automated selection of estimation of distribution algorithms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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We investigate a bi-variate probabilistic model-building GA for the graph bipartitioning problem.The graph bipartitioning problem is a grouping problem that requires some modi.cations to the standard construction of the dependency tree.We also increase the computational efficiency of the Bi-PMBGA by restricting the dependency tree to the edges of the graph to be partitioned.Experimental results indicate that the Bi-PMBGA performs signi .cantly better than the multi-start local search.Compared to a genetic local search algorithm the Bi-PMBGA performs slightly worse on some of the graphs considered here.