A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
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Adaptive filter theory (3rd ed.)
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SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
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SIAM Journal on Optimization
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Soft Computing - A Fusion of Foundations, Methodologies and Applications
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Combining competent crossover and mutation operators: a probabilistic model building approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Linear Models and Generalizations: Least Squares and Alternatives
Linear Models and Generalizations: Least Squares and Alternatives
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Substructural Surrogates for Learning Decomposable Classification Problems
Learning Classifier Systems
Structure learning and optimisation in a Markov-network based estimation of distribution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Entropy-based substructural local search for the bayesian optimization algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Entropy measurement-based estimation model for bayesian optimization algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sensibility of linkage information and effectiveness of estimated distributions
Evolutionary Computation
Entropy-based evaluation relaxation strategy for Bayesian optimization algorithm
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
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The paper presents an evaluation-relaxation scheme where a fitness surrogate automatically adapts to the problem structure and the partial contributions of subsolutions to the fitness of an individual are estimated efficiently and accurately. In particular, the probabilistic model built by extended compact genetic algorithm is used to infer the structural form of the surrogate and a least squares method is used to estimate the coefficients of the surrogate. Using the surrogate avoids the need for expensive fitness evaluation for some of the solutions, and thereby yields significant efficiency enhancement. Results show that a surrogate, which automatically adapts to problem knowledge mined from probabilistic models, yields substantial speedup (1.75--3.1) on a class of boundedly-difficult additively-decomposable problems with and without additive Gaussian noise. The speedup provided by the surrogate increases with the number of substructures, substructure complexity, and noise-to-signal ratio.