Crossover plays an important role in GA-based search. There have been many empirical comparisons of different crossover operators in the literature. However, analytical results are limited. No theory has explained the behaviours of different crossover operators satisfactorily. This paper analyses crossover from quite a different point of view from the classical schema theorem. It explains the behaviours of different crossover operators through the investigation of crossover's search neighbourhood and search step size. It is shown that given the binary chromosome encoding scheme GAs with a large search step size is better than GAs with a small step size for most problems. Since uniform crossover's search step size is larger than th...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
NK--landscapes offer the ability to assess the performance of evolutionary algorithms on problems wi...
In this paper we study and compare the search properties of different crossover operators in genetic...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
Recombination operators with high positional bias are less disruptive against adjacent genes. Theref...
The traditional crossover operator used in genetic search exhibits a position-dependent bias called ...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent em...
Genetic Algorithm (GA) has been widely used in many fields of optimization; one of them is Traveling...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex in-t...
3siIn this paper, we undertake an investigation on the effect of balanced and unbalanced crossover o...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
NK--landscapes offer the ability to assess the performance of evolutionary algorithms on problems wi...
In this paper we study and compare the search properties of different crossover operators in genetic...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
Recombination operators with high positional bias are less disruptive against adjacent genes. Theref...
The traditional crossover operator used in genetic search exhibits a position-dependent bias called ...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent em...
Genetic Algorithm (GA) has been widely used in many fields of optimization; one of them is Traveling...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex in-t...
3siIn this paper, we undertake an investigation on the effect of balanced and unbalanced crossover o...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
NK--landscapes offer the ability to assess the performance of evolutionary algorithms on problems wi...