Add to ORKGGenetic algorithms are search or classification algorithms based on natural models. They present a high degree of internal parallelism. We developed two versions, differing in the way the population is organized and we studied and compared their characteristics and performances when applied to the optimization of multidimensional function problems. All the implementations are realized on transputer networks
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Practical implementation methods for parallel computations in the genetic algorithm for discrete opt...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Parallel genetic algorithms are often very different from the "traditional" genetic algori...
An architecture of a distributed parallel genetic algorithm was developed to improve computing resou...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Many important traits in plants, animals and humans are quantitative, and most such traits are gener...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
Practical implementation methods for parallel computations in the genetic algorithm for discrete opt...
Most real-life data analysis problems are difficult to solve using exact methods, due to the size of...
Parallel genetic algorithms are often very different from the "traditional" genetic algori...
An architecture of a distributed parallel genetic algorithm was developed to improve computing resou...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
Many important traits in plants, animals and humans are quantitative, and most such traits are gener...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...