Add to ORKGThis paper examines the convergence of genetic algorithms using a cluster-analytic-type procedure. The procedure is illustrated with a hybrid genetic algorithm applied to the quadratic assignment problem. Results provide valuable insight into how population members are selected as the number of generations increases and how genetic algorithms approach stagnation after many generations. Copyright © 2006 Z. Drezner and G. A. Marcoulides. This is an open access article dis-tributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is prop-erly cited. 1
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
. Genetic algorithms are widely used as optimization and adaptation tools, and they became important...
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...
Abstract—In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optim...
In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partit...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
Genetic algorithms are extremely popular methods for solving optimization problems. They are a popul...
The rate of convergence and the structure of stable populations are studied for a simple, and yet no...
Over the last decade, variant of genetic algorithm (GA) approaches have been used to solve various t...
Population initialization is one of the important tasks in evolutionary and genetic algorithms (GAs)...
Abstract: Genetic algorithms are search and optimization techniques which have their origin and insp...
The ideal of designing a robust and efficient Genetic Algorithms (GAs), easy to use and applicable t...
Genetic algorithms apply the biological principles of selection, mutation, and crossover to a popula...
Genetic algorithms have been shown effective for solving complex optimization problems such as job s...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
. Genetic algorithms are widely used as optimization and adaptation tools, and they became important...
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...
Abstract—In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optim...
In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partit...
This article studies the convergence characteristics of a genetic algorithm (GA) in which individual...
Genetic algorithms are extremely popular methods for solving optimization problems. They are a popul...
The rate of convergence and the structure of stable populations are studied for a simple, and yet no...
Over the last decade, variant of genetic algorithm (GA) approaches have been used to solve various t...
Population initialization is one of the important tasks in evolutionary and genetic algorithms (GAs)...
Abstract: Genetic algorithms are search and optimization techniques which have their origin and insp...
The ideal of designing a robust and efficient Genetic Algorithms (GAs), easy to use and applicable t...
Genetic algorithms apply the biological principles of selection, mutation, and crossover to a popula...
Genetic algorithms have been shown effective for solving complex optimization problems such as job s...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
. Genetic algorithms are widely used as optimization and adaptation tools, and they became important...
summary:Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are proba...