AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization condition in the Markov chain theory. We classify genetic algorithms into two kinds: one with time-invariant genetic operators, another with time-variant genetic operators. For the former case, we have obtained the bound on its convergence rate on the general state space; for the later case, we have bounded its convergence rate on the finite state space
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable...
Genetic algorithms are stochastic search procedures based on randomized operators such as crossover ...
AbstractGenetic algorithms are stochastic search procedures based on randomized operators such as cr...
Convergence of genetic algorithms in the form of asymptotic stability requirements is discussed. Som...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
Abstract. The simple genetic algorithm (SGA) and its convergence analysis are main subjects of the a...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
Abstract. This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
This paper presents a lower-bound result on the computational power of a genetic algorithm in the co...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Original article can be found at: http://www.sciencedirect.com/science/journal/03043975 Copyright El...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable...
Genetic algorithms are stochastic search procedures based on randomized operators such as crossover ...
AbstractGenetic algorithms are stochastic search procedures based on randomized operators such as cr...
Convergence of genetic algorithms in the form of asymptotic stability requirements is discussed. Som...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
Abstract. The simple genetic algorithm (SGA) and its convergence analysis are main subjects of the a...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
Abstract. This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
This paper presents a lower-bound result on the computational power of a genetic algorithm in the co...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Original article can be found at: http://www.sciencedirect.com/science/journal/03043975 Copyright El...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Fitness distributions (landscapes) of programs tend to a limit as they get bigger. Markov chain conv...
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable...
Genetic algorithms are stochastic search procedures based on randomized operators such as crossover ...
AbstractGenetic algorithms are stochastic search procedures based on randomized operators such as cr...
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