A parallel implementation of Genetic Programming using PVM is described. Two different topologies for parallel implementation of GP are examined. Both of them are based on the island model for evolutionary algorithms. It is shown that considerable speedup of the GP execution can be achieved and that the parallel versions of the algorithm are very suitable for complex, time consuming problems
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms (GAs) have proved to be a very useful and flexible way to solve difficult combina...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
A new parallel implementation of genetic programming based on the cellular model is presented and co...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
The thesis describes design and implementation of various evolutionary algorithms, which were enhanc...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
There is a lack of a programming free solution which can run a distributed genetic algorithm in para...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfiel...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Genetic algorithms (GAs) have proved to be a very useful and flexible way to solve difficult combina...
Genetic algorithms are modern algorithms intended to solve optimization problems. Inspiration origin...
As genetic algorithms (GAs) are used to solve harder problems, it is becoming necessary to use bette...
Evolutionary algorithms have been gaining increased attention the past few years because of their ve...
A new parallel implementation of genetic programming based on the cellular model is presented and co...
Parallel genetic algorithms are usually implemented on parallel machines or distributed systems. Thi...
The thesis describes design and implementation of various evolutionary algorithms, which were enhanc...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
There is a lack of a programming free solution which can run a distributed genetic algorithm in para...
Genetic Algorithms contain natural parallelism. There are two main approaches in parallelising GAs. ...
Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfiel...
Genetic algorithms are search or classification algorithms based on natural models. They present a h...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
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