WOS: 000281591300087In this study, two new crossover operators in genetic algorithm are proposed; sequential and random mixed crossover. In the first stage, existing and developed crossover operators were applied to two benchmark problems, namely the reinforced concrete beam problem and the space truss problem. In the second stage, the developed crossover operators were applied to a deep beam problem and, a concrete mix design problem. The fittest values obtained using developed crossover operators were higher than those were obtained with existing crossover operator after 30,000 generations. Moreover, in developed crossover operators, the random mixed crossover yields higher fitness values than the existing crossover operators. (c) 2010 El...
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techni...
This article aims at studying the behavior of different types of crossover operators in the performa...
Holland’s analysis of the sources of power of genetic algorithms has served as guidance for the appl...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Holland's analysis of the sources of power of genetic algorithms has served as guidance for the...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. T...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Char...
This paper presents a comparison in the performance analysis between a newly developed crossover ope...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
ABSTRACT Genetic Algorithms (GAs) are a set of local search algorithms that are based on principles ...
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techni...
This article aims at studying the behavior of different types of crossover operators in the performa...
Holland’s analysis of the sources of power of genetic algorithms has served as guidance for the appl...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Holland's analysis of the sources of power of genetic algorithms has served as guidance for the...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. T...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Char...
This paper presents a comparison in the performance analysis between a newly developed crossover ope...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
ABSTRACT Genetic Algorithms (GAs) are a set of local search algorithms that are based on principles ...
Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techni...
This article aims at studying the behavior of different types of crossover operators in the performa...
Holland’s analysis of the sources of power of genetic algorithms has served as guidance for the appl...