Genetic algorithms (GAs) are a powerful set of search techniques that have elicited a great deal of interest and research in the fields of computer science and computer engineering over the past decade. The class of problems to which these algorithms can be applied are often computationally intensive; consuming large amounts of computer space-time. Thus, one important area of research with regard to GAs is concerned with creating parallel implementations. The design and testing of a library for island model parallel GAs (MPIGALib) is presented as well as some interesting results from its application to th
This paper considers the most simple type of parallel GA: a single-population master-slave implement...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Genetic algorithms (GAs) have proved to be a very useful and flexible way to solve difficult combina...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
This paper considers the most simple type of parallel GA: a single-population master-slave implement...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Genetic algorithms (GAs) have proved to be a very useful and flexible way to solve difficult combina...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
A parallel implementation of Genetic Programming using PVM is described. Two different topologies fo...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
This paper considers the most simple type of parallel GA: a single-population master-slave implement...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...