In this article we present the implementation and formal verification, using the Coq system [FHB+98], of a generalized version of the crossover operator applied to genetic algorithms (GA) [Hol92]. The first part of this work defines the multiple crossover ⊗`(p, q) of two lists p, q in any finite number of points. This definition generalizes the one given in [UE99] for a maximum number of six points. In the second part, we show that this crossover operator does not depend on the order of the list of points. Then, a more efficient definition of crossover `(p, q) is provided in the third part. Finally, we formally establish the exact relation between these two definitions, using the notion of differences list: ⊗`(p, q) = (dif `)(p, q). 1
In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. T...
4siThe theoretical study of Genetic Algorithms and the dynamics induced by their genetic operators i...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among...
In this paper we present some theoretical results on two forms of multi-point crossover: n-point cro...
WOS: 000281591300087In this study, two new crossover operators in genetic algorithm are proposed; se...
Traditionally, crossover operators are based on combination--an operator takes parts from two parent...
Genetic Algorithms is a population-based optimization strategy that has been successfully applied to...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
In recent theoretical and experimental work on schemata in genetic programming we have proposed a ne...
In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. T...
4siThe theoretical study of Genetic Algorithms and the dynamics induced by their genetic operators i...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract(i) We investigate spectral and geometric properties of the mutation-crossover operator in a...
Crossover is the main genetic operator which influences the power of evolutionary algorithms. Among...
In this paper we present some theoretical results on two forms of multi-point crossover: n-point cro...
WOS: 000281591300087In this study, two new crossover operators in genetic algorithm are proposed; se...
Traditionally, crossover operators are based on combination--an operator takes parts from two parent...
Genetic Algorithms is a population-based optimization strategy that has been successfully applied to...
The genetic algorithm (GA) is an optimization and search technique based on the principles of geneti...
In recent theoretical and experimental work on schemata in genetic programming we have proposed a ne...
In this paper a new crossover operator called the double distribution crossover (DDX) is proposed. T...
4siThe theoretical study of Genetic Algorithms and the dynamics induced by their genetic operators i...
Abstract. The performance of a genetic algorithm (GA) is dependent on many factors: the type of cros...