Data structures and network algorithms
Data structures and network algorithms
Mathematical preliminaries for computer networking
Mathematical preliminaries for computer networking
Learning Bayesian networks with local structure
Learning in graphical models
Detecting spin-flip symmetry in optimization problems
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
The Effect of Spin-Flip Symmetry on the Performance of the Simple GA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
New Optimization Algorithms in Physics
New Optimization Algorithms in Physics
Phase Transitions in Combinatorial Optimization Problems - Basics, Algorithms and Statistical Mechanics
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Hierarchical BOA solves ising spin glasses and MAXSAT
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Correlation guided model building
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of evolutionary algorithms on the one-dimensional spin glass with power-law interactions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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This paper analyzes the hierarchical Bayesian optimization algorithm (hBOA) on the problem of finding ground states of Ising spin glasses with ±J couplings in two and three dimensions. The performance of hBOA is compared to that of the simple genetic algorithm (GA) and the univariate marginal distribution algorithm (UMDA). The performance of all tested algorithms is improved by incorporating a deterministic hill climber (DHC) based on single-bit flips and cluster exact approximation (CEA). The results show that hBOA significantly outperforms GA and UMDA with both types of local search and that CEA enables all tested algorithms to solve larger spin-glass instances than DHC. Using advanced hybrid methods created by combining competent genetic and evolutionary algorithms with advanced local searchers thus proves advantageous in this challenging class of problems.