Local search is mainly implemented by the reproduction and crossover operation, while global search is assured by the mutation operation in conventional genetic algorithm. In order to enhance the global search ability, three new mutation operators are proposed based on the idea that big change into small and small change into big for gene bit selected at random. The experimental verification shows that the proposed new genetic algorithms with new mutation operators are effective in seeking for the global optimal solutions
An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization probl...
Genetic algorithms play a significant role, as search techniques for handling complex spaces, in man...
New genetic operators are described that assure preservation of the feasibility of candidate solutio...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms are adaptive methods based on natural evolution which may be used for search and ...
Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutio...
Recent development of large databases, especially those in genetics and proteomics, is pushing the d...
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetic...
The paper discusses the possibility to manage search direction in genetic algorithm crossover and mu...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Abstract- Self-adaptation in evolutionary computation refers to the encoding of parameters into the ...
In this paper, we propose a selective mutation method for improving the performances of genetic algo...
In this study, a new crossover approach to the real-coded genetic algorithm is proposed. The approac...
Abstract The process of mutation has been studied extensively in the field of biology and it has bee...
This paper discusses the possibility of managing search direction in genetic algorithm crossover and...
An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization probl...
Genetic algorithms play a significant role, as search techniques for handling complex spaces, in man...
New genetic operators are described that assure preservation of the feasibility of candidate solutio...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
Genetic algorithms are adaptive methods based on natural evolution which may be used for search and ...
Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutio...
Recent development of large databases, especially those in genetics and proteomics, is pushing the d...
Genetic algorithm is a method of optimization based on the concepts of natural selection and genetic...
The paper discusses the possibility to manage search direction in genetic algorithm crossover and mu...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Abstract- Self-adaptation in evolutionary computation refers to the encoding of parameters into the ...
In this paper, we propose a selective mutation method for improving the performances of genetic algo...
In this study, a new crossover approach to the real-coded genetic algorithm is proposed. The approac...
Abstract The process of mutation has been studied extensively in the field of biology and it has bee...
This paper discusses the possibility of managing search direction in genetic algorithm crossover and...
An improved real-coded genetic algorithm (IRCGA) is proposed to solve constrained optimization probl...
Genetic algorithms play a significant role, as search techniques for handling complex spaces, in man...
New genetic operators are described that assure preservation of the feasibility of candidate solutio...