Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Introduction to algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
The nature of statistical learning theory
The nature of statistical learning theory
Epistasis in genetic algorithms revisited
Information Sciences: an International Journal
Evolutionary algorithms: from recombination to search distributions
Theoretical aspects of evolutionary computing
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
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
Induction: Processes of Inference, Learning, and Discovery
Induction: Processes of Inference, Learning, and Discovery
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Fixed Point Analysis Of A Gene Pool GA With Mutation
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
A Genetic Model and the Hopfield Networks
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
A genetic system based on simulated crossover of sequences of two-bit genes
Theoretical Computer Science
The equation for response to selection and its use for prediction
Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Parallel Implementation of EDAs Based on Probabilistic Graphical Models
IEEE Transactions on Evolutionary Computation
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We take into account the problem of extending the Univariate Marginal Distribution Genetic Algorithm (UMDGA) modeling and analysis to the multivariate framework. In particular, we introduce the basic general concepts and mathematical formalism to devise genetic algorithms useful to solve problems involving dependencies among genes. We state the relationships between the natural component attractors of the (numerous or infinite population) multivariate marginal distribution genetic systems and the equilibrium points of associated neural networks so rephrasing the problem of solving an evolutionary task in terms of the analysis of its properties through suitably designed neural networks.