Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning Bayesian networks with local structure
Learning in graphical models
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
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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Sporadic model building for efficiency enhancement of hierarchical BOA
Proceedings of the 8th 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
Performance of network crossover on NK landscapes and spin glasses
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
A test problem with adjustable degrees of overlap and conflict among subproblems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Explaining adaptation in genetic algorithms with uniform crossover: the hyperclimbing hypothesis
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Explaining optimization in genetic algorithms with uniform crossover
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
| Hi-index | 0.00 |
This study focuses on the problem of finding ground states of random instances of the Sherrington-Kirkpatrick (SK) spin-glass model with Gaussian couplings. While the ground states of SK spin-glass instances can be obtained with branch and bound, the computational complexity of branch and bound yields instances of not more than approximately 90 spins. We describe several approaches based on the hierarchical Bayesian optimization algorithm (hBOA) to reliably identify ground states of SK instances intractable with branch and bound, and present a broad range of empirical results on such problem instances. We argue that the proposed methodology holds a big promise for reliably solving large SK spin-glass instances to optimality with practical time complexity. The proposed approaches to identifying global optima reliably can also be applied to other problems and can be used with many other evolutionary algorithms. Performance of hBOA is compared to that of the genetic algorithm with two common crossover operators.