Convergence of genetic algorithms in the form of asymptotic stability requirements is discussed. Some tools to measure convergence properties of genetic algorithms are introduced. A classification procedure is proposed that is based on the following conjecture: the entropy and the fractal dimension of trajectories of genetic algorithms produced by them are quantities that can characterize the algorithms. The role of these quantities as invariants of the algorithm classes is discussed together with the compression ratio of points of genetic algorithms.Stefan Kotowski, Witold Kosinski, Zbigniew Michalewicz, Piotr Synak and Łukasz Brock
The rate of convergence and the structure of stable populations are studied for a simple, and yet no...
This paper presents a lower-bound result on the computational power of a genetic algorithm in the co...
This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. T...
Abstract—Some tools to measure convergence properties of genetic algorithms are introduced. A classi...
Convergence properties of genetic algorithms are investigated. For them some measures are introduced...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs).Gen...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
Abstract. The simple genetic algorithm (SGA) and its convergence analysis are main subjects of the a...
Considerable empirical results have been reported on the computational performance of genetic algori...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
In this thesis a general mathematical framework to describe evolutionary algorithms is developed. Th...
The rate of convergence and the structure of stable populations are studied for a simple, and yet no...
This paper presents a lower-bound result on the computational power of a genetic algorithm in the co...
This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. T...
Abstract—Some tools to measure convergence properties of genetic algorithms are introduced. A classi...
Convergence properties of genetic algorithms are investigated. For them some measures are introduced...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
This paper surveys strategies applied to avoid premature convergence in Genetic Algorithms (GAs).Gen...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
This paper presents a theoretical analysis of the convergence conditions for evolutionary algorithms...
Abstract. The simple genetic algorithm (SGA) and its convergence analysis are main subjects of the a...
Considerable empirical results have been reported on the computational performance of genetic algori...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
In this thesis a general mathematical framework to describe evolutionary algorithms is developed. Th...
The rate of convergence and the structure of stable populations are studied for a simple, and yet no...
This paper presents a lower-bound result on the computational power of a genetic algorithm in the co...
This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. T...