Add to ORKGGenetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases the GA has been adapted to the hardware structure of the system. This paper describes the implementation of a standard genetic algorithm on several MIMD multiprocessor systems. It discusses the data dependencies of the different parts of the algorithm and the changes necessary to adapt the serial version to the parallel versions. Timing measurements and speedups are given for a common problem implemented on all machines
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. The...
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may be...
Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases...
This paper describes the implementation of a standard genetic algorithm (GA) on the MIMD multiproces...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
AbstractThis paper presents a network parallel genetic algorithm for the one machine sequencing prob...
This paper presents an implementation of three Genetic Algorithm models for solving a reliability op...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solu...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
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 are modern algorithms intended to solve optimization problems. Inspiration origin...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. The...
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may be...
Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases...
This paper describes the implementation of a standard genetic algorithm (GA) on the MIMD multiproces...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
AbstractThis paper presents a network parallel genetic algorithm for the one machine sequencing prob...
This paper presents an implementation of three Genetic Algorithm models for solving a reliability op...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solu...
Abstract- In this paper we propose the implementation of a massively parallel GP model in hardware i...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
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 are modern algorithms intended to solve optimization problems. Inspiration origin...
Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to...
We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. The...
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may be...