Title :
A parallel genetic algorithm on the CM-2 for multi-modal optimization
Author_Institution :
Media Lab., MIT, Cambridge, MA, USA
Abstract :
A genetic algorithm is an optimization method well suited to be implemented on a SIMD machine; it deals with a population of individuals (Multiple Data) that evolve in parallel and undergo the same operations (Single Instruction). This paper presents a genetic algorithm with a dynamic division mechanism conceived on the Connection Machine-2 to treat multimodal optimization problems, i.e. search spaces with multiple maxima. The general idea of the algorithm is to dynamically divide the population into an increasing number of subpopulations to allow specialization on the different maxima discovered during the search process. The method is flexible because it requires practically no a-priori knowledge about the fitness function. Results of applications to multi-modal two-dimensional landscapes are presented
Keywords :
genetic algorithms; optimisation; parallel algorithms; parallel machines; CM-2; Connection Machine-2; SIMD machine; dynamic division mechanism; fitness function; multimodal optimization; multimodal optimization problems; multimodal two-dimensional landscapes; multiple data; optimization method; parallel genetic algorithm; search process; search spaces; single instruction; Algorithm design and analysis; Encoding; Genetic algorithms; Heuristic algorithms; Laboratories; Optimization methods; Processor scheduling; Sampling methods; Scheduling algorithm; Stochastic processes;
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
DOI :
10.1109/ICEC.1994.349950
