Abstract— In this paper we evaluate the effectiveness of three different distributed genetic algorithms (DGAs). The first one is DGA with Diversity Guided Migration, second one is DGA with Automated Adaptive Migration and the last one is DGA with Bicoded chromosomes and confidence rates. All these algorithms were investigated to improve the overall quality of solutions in the distributed genetic algorithm for different problems. Our comparison between those algorithms depended on some important factors; like, achieving diversity in selecting individuals, process of replacing the individuals between subpopulations, computational time and memory space. As a result, DGA with Diversity Guided Migration (DGM), was nominated to be better than the...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
An architecture of a distributed parallel genetic algorithm was developed to improve computing resou...
In this paper, we present a detailed analysis of the applica-tion of Genetic Programming to the evol...
In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs...
This paper discusses the effect of randomization of migration rate in distributed genetic algorithms...
The genetic algorithm is a general purpose, population-based search algorithm in which the individua...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can...
This paper presents a dynamic adaptation scheme for the frequency of inter-deme migration in distrib...
Abstract In this study, a new model of distributed genetic algorithm (DGA) for cluster systems is pr...
Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the...
In this work a Genetic Algorithm coding and a required genetic operation library has been developed ...
AbstractThis paper presents a distributed genetic algorithm for the discovery of classification rule...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
An architecture of a distributed parallel genetic algorithm was developed to improve computing resou...
In this paper, we present a detailed analysis of the applica-tion of Genetic Programming to the evol...
In this paper we evaluates the effectiveness of three different distributed genetic algorithms (DGAs...
This paper discusses the effect of randomization of migration rate in distributed genetic algorithms...
The genetic algorithm is a general purpose, population-based search algorithm in which the individua...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Many distributed systems (task scheduling, moving priorities, changing mobile environments, ...) can...
This paper presents a dynamic adaptation scheme for the frequency of inter-deme migration in distrib...
Abstract In this study, a new model of distributed genetic algorithm (DGA) for cluster systems is pr...
Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the...
In this work a Genetic Algorithm coding and a required genetic operation library has been developed ...
AbstractThis paper presents a distributed genetic algorithm for the discovery of classification rule...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Abstract — Genetic algorithm (GA), as an important intelligence computing tool, is a wide research c...
An architecture of a distributed parallel genetic algorithm was developed to improve computing resou...
In this paper, we present a detailed analysis of the applica-tion of Genetic Programming to the evol...