Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. The first one is parallelising individual functional components of a standard, sequential GA. The only difference with the sequential GA is in the computation speed. The second approach more closely resembles the real life simultaneous evolution of species, which is the central theme in GAs. Algorithms following this approach are still referred to as GAs but are different from Holland's standard GA. For these algorithms it is not the improvement in computation speed that is the driving factor, but the efficiency with which they search a given solution space. We describe a number of the most common parallel GA methods found in the literature a...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
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...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Genetic algorithms (GAs) are a powerful set of search techniques that have elicited a great deal of ...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
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...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Genetic algorithms (GAs) are a powerful set of search techniques that have elicited a great deal of ...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...