Add to ORKGParallel genetic algorithms (PGA) use two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently in its neighborhood. Secondly, each individual may improve its fitness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of intelligent and active individuals, whereas a GA uses a large population of passive individuals. We will show the power of the PGA with two combinatorial problems - the traveling salesman problem and the m graph partitioning problem. In th...
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm....
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
In this paper, we review parallel search techniques for approximating the global optimal solution of...
Many optimization problems have complex search space, which either increase the solving problem time...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
With combinatorial optimization we try to find good solutions for many computationaly difficult prob...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
The efficient implementation of parallel processing architectures generally requires the solution of...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...
The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm....
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We...
In this paper, we review parallel search techniques for approximating the global optimal solution of...
Many optimization problems have complex search space, which either increase the solving problem time...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not hav...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
With combinatorial optimization we try to find good solutions for many computationaly difficult prob...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
The efficient implementation of parallel processing architectures generally requires the solution of...
Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for ...
A parallel genetic algorithm for the graph partitioning problem is presented, which combines general...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competiti...