In this paper, a new genetic operator designed for function optimization with binary encoding is presented. This operator carries out logical and and or operation on some corresponding bits oftwo chromosomes and produces two new children. We proved that this operator combines the features of both crossover and mutation, and that it works in a way consistent with schema theorem. The effectiveness was demonstrated by experimental results
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
. This work deals with the problem of function learning by genetic algorithms where the function is ...
Abstract The process of mutation has been studied extensively in the field of biology and it has bee...
Genetic algorithm (GA) is a well known algorithm applied to a wide variety of optimization problems ...
Abstract — A new genetic operator is proposed in the context of Genetic Algorithms that are applied ...
The genetic code of amino acid sequences in proteins does not allow understanding and modeling of in...
Abstract—In this contribution, the use of a new genetic operator is proposed. The main advantage of ...
Genetic algorithms and genetic programming are optimization methods in which potential solutions evo...
New genetic operators are described that assure preservation of the feasibility of candidate solutio...
Optimization of digital circuits still attracts much attention not only of researchers but mainly ch...
The mutation and cross-over operators are, with selection, the foundation of genetic algorithms. We ...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
Different variants of genetic operators are introduced and compared for linear genetic programming i...
The behavior of the two-point crossover operator, on candidate solutions to an optimization problem ...
The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
. This work deals with the problem of function learning by genetic algorithms where the function is ...
Abstract The process of mutation has been studied extensively in the field of biology and it has bee...
Genetic algorithm (GA) is a well known algorithm applied to a wide variety of optimization problems ...
Abstract — A new genetic operator is proposed in the context of Genetic Algorithms that are applied ...
The genetic code of amino acid sequences in proteins does not allow understanding and modeling of in...
Abstract—In this contribution, the use of a new genetic operator is proposed. The main advantage of ...
Genetic algorithms and genetic programming are optimization methods in which potential solutions evo...
New genetic operators are described that assure preservation of the feasibility of candidate solutio...
Optimization of digital circuits still attracts much attention not only of researchers but mainly ch...
The mutation and cross-over operators are, with selection, the foundation of genetic algorithms. We ...
Abstract. Genetic algorithms (GAs) generate solutions to optimization problems using techniques insp...
Different variants of genetic operators are introduced and compared for linear genetic programming i...
The behavior of the two-point crossover operator, on candidate solutions to an optimization problem ...
The application of evolutionary algorithms (EAs) requires as a basic design decision the choice of a...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
. This work deals with the problem of function learning by genetic algorithms where the function is ...
Abstract The process of mutation has been studied extensively in the field of biology and it has bee...