A study of a non-linear optimization problem using a distributed genetic algorithm
- 24 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2 (01903918) , 29-36
- https://doi.org/10.1109/icpp.1996.537378
Abstract
Genetic algorithms have been used successfully as a global optimization method when the search space is very large. To characterize and analyze the performance of genetic algorithms on a cluster of workstations, a parallel version of the GENESIS 5.0 was developed using PVM 3.3. This version, called VMGENESIS, was used to study a nonlinear least-squares problem. Performance results show that linear speedups can be achieved if the basic distributed genetic algorithm is combined with a simple dynamic load-balancing mechanism. Results also show that the quality of search changes significantly with the number of processors involved in the computation and with the frequency of communication.Keywords
This publication has 9 references indexed in Scilit:
- Comparison of global search methods for design optimization using simulationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Parallel recombinative simulated annealing: A genetic algorithmParallel Computing, 1995
- Genetic algorithms. Simulating nature's methods of evolving the best design solutionIEEE Potentials, 1995
- Genetic algorithms: a surveyComputer, 1994
- Bounding the probability of success of stochastic methods for global optimizationComputers & Mathematics with Applications, 1993
- Is beauty in the eye of the beholder?Ethology and Sociobiology, 1993
- The parallel genetic algorithm as function optimizerParallel Computing, 1991
- Optimization of Control Parameters for Genetic AlgorithmsIEEE Transactions on Systems, Man, and Cybernetics, 1986
- Optimization by Simulated AnnealingScience, 1983