This paper presents an implementation of three Genetic Algorithm models for solving a reliability optimization problem for a redundancy system with several failure modes, a modification on a parallel a genetic algorithm model and a new parallel genetic algorithm model. These three models are: a sequential model, a modified global parallel genetic algorithm model and a new proposed parallel genetic algorithm model we called the Trigger Model (TM). The reduction of the implementation processing time is the basic motivation of genetic algorithms parallelization. In this work, parallel virtual machine (PVM), which is a portable message-passing programming system, designed to link separate host machines to form a virtual machine which is a singl...
The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and en...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
This paper presents an implementation of three Genetic Algorithm models for solving a reliability op...
AbstractThis paper presents a network parallel genetic algorithm for the one machine sequencing prob...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
This paper describes and verifies a convergence model that allows the islands in a parallel genetic ...
Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases...
A new coarse grain parallel genetic algorithm (PGA) and a new implementation of a data-parallel GA a...
The parallel genetic algorithms implementation for neural networks models construction is discussed....
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and en...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...
This paper presents an implementation of three Genetic Algorithm models for solving a reliability op...
AbstractThis paper presents a network parallel genetic algorithm for the one machine sequencing prob...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
This paper describes and verifies a convergence model that allows the islands in a parallel genetic ...
Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases...
A new coarse grain parallel genetic algorithm (PGA) and a new implementation of a data-parallel GA a...
The parallel genetic algorithms implementation for neural networks models construction is discussed....
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The genetic algorithm paradigm is a well-known heuristic for solving many problems in science and en...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
Genetic algorithms are frequently used to solve optimization problems. However, the problems become ...