Abstract—Some tools to measure convergence properties of genetic algorithms are introduced. A classification procedure is proposed for genetic algorithms based on a conjecture: the entropy and the fractal dimension of trajectories produced by them are quantities that characterize the classes of the algorithms. The role of these quantities as invariants of the algorithm classes is discussed together with the compression ratio of points of the genetic algorithm. I
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic a...
Convergence of genetic algorithms in the form of asymptotic stability requirements is discussed. Som...
Convergence properties of genetic algorithms are investigated. For them some measures are introduced...
Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic alg...
Genetic Algorithms (GAs) are direct searching methods which require little information from design s...
In this thesis a general mathematical framework to describe evolutionary algorithms is developed. Th...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
The paper describes an approach to measuring convergence of an algorithm to its result in terms of a...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
Genetic algorithms provide an approach to learning that is based loosely on simulated evolution. Hyp...
This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. T...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
This document contains a selection of research works to which I have contributed. It is structured a...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic a...
Convergence of genetic algorithms in the form of asymptotic stability requirements is discussed. Som...
Convergence properties of genetic algorithms are investigated. For them some measures are introduced...
Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic alg...
Genetic Algorithms (GAs) are direct searching methods which require little information from design s...
In this thesis a general mathematical framework to describe evolutionary algorithms is developed. Th...
AbstractThis paper discusses the convergence rates of genetic algorithms by using the minorization c...
The paper describes an approach to measuring convergence of an algorithm to its result in terms of a...
It is difficult to predict a genetic algorithm's behavior on an arbitrary problem. Combining ge...
Genetic algorithms provide an approach to learning that is based loosely on simulated evolution. Hyp...
This paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. T...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...
This document contains a selection of research works to which I have contributed. It is structured a...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic...
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic a...