Abstract. This paper investigates genetic drift in multi-parent genetic algorithms (MPGAs). An exact model based on Markov chains is pro-posed to formulate the variation of gene frequency. This model iden-tifies the correlation between the adopted number of parents and the mean convergence time. Moreover, it reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs. The good fit between theoretical and experi-mental results further verifies the capability of this model.
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to ...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Abstract — This paper presents a Markov model for the conver-gence of multi-parent genetic algorithm...
In this paper we study random genetic drift in a finite genetic population. Exact formulae for calcu...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
The most common phenomena in the evolution process are natural selection and genetic drift. In this ...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
In this paper we present an analysis of gene pool recombination in genetic algorithms in the context...
Recent research shows that enlarging the arity of recombination operators in a Genetic Algorithm low...
A comparison is made between the dynamics of steady state and generational genetic algorithms using ...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs).Gen...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to ...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...
Abstract — This paper presents a Markov model for the conver-gence of multi-parent genetic algorithm...
In this paper we study random genetic drift in a finite genetic population. Exact formulae for calcu...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
The most common phenomena in the evolution process are natural selection and genetic drift. In this ...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
In this paper we present an analysis of gene pool recombination in genetic algorithms in the context...
Recent research shows that enlarging the arity of recombination operators in a Genetic Algorithm low...
A comparison is made between the dynamics of steady state and generational genetic algorithms using ...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs).Gen...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Finite Markov models of the evolution of finite populations can be used as a tool to study the theor...
Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to ...
AbstractThis paper presents stochastic models for two classes of Genetic Algorithms. We present impo...