This paper presents a performance study of a parallel, coarse-grained, multiple-deme Genetic Algorithm (GA) with adaptive mutation. The effect of varying migration period and number of subpopulations upon the GA is evaluated. Using common unimodal and multimodal objective functions, this study measures the convergence velocity and solution quality for the proposed genetic algorithm. In this paper, we briefly survey previous work in static and adaptive control parameters and parallel genetic algorithms (PGAs). Experimental results show that migration period and the number of subpopulations significantly influence the performance of the genetic algorithm
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
Abstract — In this paper, a parallel model of multi-objective genetic algorithm supposing a grid env...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Abstract Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the ...
. A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergenc...
This paper presents a dynamic adaptation scheme for the frequency of inter-deme migration in distrib...
Abstract—In this paper an improved adaptive parallel genetic algorithm is proposed to solve problems...
This paper extends previous analyses of parallel GAs with multiple populations (demes) to consider c...
The genetic algorithm is a general purpose, population-based search algorithm in which the individua...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parame...
This paper presents GAP/D, a VLSI implementation of a dynamic adaptation scheme for the frequency of...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
Abstract — In this paper, a parallel model of multi-objective genetic algorithm supposing a grid env...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...
migration strategy; Abstract. Genetic Algorithm (GA) is a powe rful global optimization search algo ...
Abstract Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the ...
. A parallel two-level evolutionary algorithm which evolves genetic algorithms of maximum convergenc...
This paper presents a dynamic adaptation scheme for the frequency of inter-deme migration in distrib...
Abstract—In this paper an improved adaptive parallel genetic algorithm is proposed to solve problems...
This paper extends previous analyses of parallel GAs with multiple populations (demes) to consider c...
The genetic algorithm is a general purpose, population-based search algorithm in which the individua...
The main aim of this thesis is the comparison of parallel and sequential algorithm implementation fo...
Parallel genetic algorithms (PGAs) have been traditionally used to extend the power of serial geneti...
This paper presents a fine-grained parallel genetic algorithm with mutation rate as a control parame...
This paper presents GAP/D, a VLSI implementation of a dynamic adaptation scheme for the frequency of...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our mo-tiva...
In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivat...
Abstract — In this paper, a parallel model of multi-objective genetic algorithm supposing a grid env...
ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to sol...