Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Search, polynomial complexity, and the fast messy genetic algorithm
Search, polynomial complexity, and the fast messy genetic algorithm
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
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
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
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
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Extracted global structure makes local building block processing effective in XCS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Probabilistic modeling for continuous EDA with Boltzmann selection and Kullback-Leibeler divergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Cross entropy and adaptive variance scaling in continuous EDA
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Introducing assignment functions to Bayesian optimization algorithms
Information Sciences: an International Journal
Voronoi-initializated island models for solving real-coded deceptive problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Bio-inspired enhancement of reputation systems for intelligent environments
Information Sciences: an International Journal
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This paper describes an evolutionary algorithm for optimization of continuous problems that combines advanced recombination techniques for discrete representations with advanced mutation techniques for continuous representations. Discretization is used to transform solutions between the discrete and continuous domains. The proposed algorithm combines the strengths of purely continuous and purely discrete approaches and eliminates some of their disadvantages. The paper tests the proposed algorithm with the recombination operator of the Bayesian optimization algorithm, σ-self-adaptive mutation, and three discretization methods. The empirical results on three problems suggest that the tested variant of the algorithm scales up well on all tested problems, indicating good scalability over a broad range of continuous problems.