Add to ORKGIn this work, a new model based on dominant gene selection operator is proposed for the genetic algorithm. The performance of the proposed model is evaluated for the well-known continuous test problems and then its performance is compared to that of standard genetic algorithm. From the results, it was seen that the proposed approach improves the performance of the standard genetic algorithm
Genetic algorithms (GAs) are stochastic search methods that mimic natural biological evolution. Gene...
A great deal of recent research has focused on the challenging task of selecting differentially expr...
Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural s...
This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to dea...
Abstract. a new genetic algorithm is proposed in the paper. Different from other genetic algorithms,...
This paper faces the problem of variables selection through the use of a genetic algorithm based met...
The performance of a Genetic Algorithm (GA) is inspired by a number of factors: the choice of the se...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
[[abstract]]According to the results of previous research, the genetic algorithm (GA) is a good tech...
For more than two decades, genetic algorithms (GAs) have been studied by researchers from different ...
In this paper, we propose a selective mutation method for improving the performances of genetic algo...
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The ...
This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic envir...
This paper presents a method on how to estimate main effects of gene representation. This estimate c...
Genetic Algorithms (GAs) have proven to be a useful means of finding optimal or near optimal solutio...
Genetic algorithms (GAs) are stochastic search methods that mimic natural biological evolution. Gene...
A great deal of recent research has focused on the challenging task of selecting differentially expr...
Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural s...
This paper proposes a new idea, namely genetic algorithms with dominant genes (GADG) in order to dea...
Abstract. a new genetic algorithm is proposed in the paper. Different from other genetic algorithms,...
This paper faces the problem of variables selection through the use of a genetic algorithm based met...
The performance of a Genetic Algorithm (GA) is inspired by a number of factors: the choice of the se...
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fit...
[[abstract]]According to the results of previous research, the genetic algorithm (GA) is a good tech...
For more than two decades, genetic algorithms (GAs) have been studied by researchers from different ...
In this paper, we propose a selective mutation method for improving the performances of genetic algo...
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The ...
This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic envir...
This paper presents a method on how to estimate main effects of gene representation. This estimate c...
Genetic Algorithms (GAs) have proven to be a useful means of finding optimal or near optimal solutio...
Genetic algorithms (GAs) are stochastic search methods that mimic natural biological evolution. Gene...
A great deal of recent research has focused on the challenging task of selecting differentially expr...
Genetic algorithms (GAs) are population-based optimisation tools inspired by evolution and natural s...