Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An algorithm for finding Hamilton cycles in random directed graphs
Journal of Algorithms
Finding hidden Hamiltonian cycles
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
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
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
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
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
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
A Genetic Model: Analysis and Application to MAXSAT
Evolutionary Computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
A genetic system based on simulated crossover of sequences of two-bit genes
Theoretical Computer Science
Modeling simple genetic algorithms
Evolutionary Computation
The equation for response to selection and its use for prediction
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
On multivariate genetic systems
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
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We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound on population size is exhibited stating that the behavior of the finite population system, in the case of sufficiently large sizes, can be approximated by the behavior of the corresponding infinite population system. Assumptions on fitness and individual chromosomes are provided implying that the behavior of the finite population genetic system remains consistent with the behavior of the associated infinite population genetic system for suitably long trajectories. The attractors (with binary components) of the infinite population genetic system are characterized as equilibrium points of a discrete (neural network) system that can be considered as a variant of a Hopfield's network; it is shown that the fitness is a Lyapunov function for the variant of the discrete Hopfield's net. Our main result can be summarized by stating that the relation between marginal distribution genetic systems and neural nets is much more general than that already shown elsewhere for other simpler models.