24/01/07The genetic algorithm (GA), is based on the mechanisms of coding, selection, crossover, mutation and insertion. Each mechanism presents several choices to giving result to several GA's variants. We obtained a better variant of genetic algorithm for resolving Travelling Salesman Problem. In this GA's variant, we introduced our new crossover operator Cedrx which when coupled with crossover operator edrx gave good results. This led to the creation of effective variants of genetic algorithm for resolving the following problems:+ Job Shop Scheduling Problem (JSSP); + Aircraft Landing Problem (ALP);+ Scheduling Problem of Vehicles on a production line in a factory (SPV).L'algorithme génétique (AG), est fondé sur les méanismes de codage, s...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Char...
This paper addresses an attempt to evolve genetic algorithms by a particular genetic programming met...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
This paper includes a flexible method for solving the travelling salesman problem using genetic algo...
Genetic Algorithms use life as their model to solve difficult problems in computer science. They use...
This paper is the result of a literature study carried out by the authors. It is a review of the dif...
The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial opt...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
The Travelling Salesman Problem (TSP) is a well-known and important combinatorial optimization probl...
The Travelling Salesman Problem (TSP) is a well-known and important combinatorial optimization probl...
Genetic algorithms simulate the survival of the fittest among individuals over consecutive generatio...
Abstract — Genetic Algorithm (GA) is an optimization method that not only used to find the shortest...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Char...
This paper addresses an attempt to evolve genetic algorithms by a particular genetic programming met...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
This paper includes a flexible method for solving the travelling salesman problem using genetic algo...
Genetic Algorithms use life as their model to solve difficult problems in computer science. They use...
This paper is the result of a literature study carried out by the authors. It is a review of the dif...
The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial opt...
This paper presents the results of an analysis of three algorithms for the Travelling Salesman Probl...
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort t...
Abstract — Genetic Algorithms are the population based search and optimization technique that mimic ...
The Travelling Salesman Problem (TSP) is a well-known and important combinatorial optimization probl...
The Travelling Salesman Problem (TSP) is a well-known and important combinatorial optimization probl...
Genetic algorithms simulate the survival of the fittest among individuals over consecutive generatio...
Abstract — Genetic Algorithm (GA) is an optimization method that not only used to find the shortest...
Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level ...
Genetic algorithm (GA) is a popular technique of optimization that is bio-inspired and based on Char...
This paper addresses an attempt to evolve genetic algorithms by a particular genetic programming met...