Genetic algorithms are widely used in engineering, to solve nonlinear, multi-target optimization problems with multiple variables (e.g. optimization of geometry of flow domains, parameters of control systems). The parallelization of software using genetic algorithms is very important because in a typical practical problem they need huge computational power. Fortunately it is easy to implement a master-slave style parallelization. Our goal was to investigate the effect of random errors that can occur in a cluster of workstations on the efficiency of the genetic algorithm.
Horváth, A., Horváth Z. Optimal shape design of diesel intake ports with evolutionary algorithm,
Proceedings of 5th European conference on numerical mathematics and advanced applications
(ENUMATH 2003) Edited by Feistauer, M. et al., Springer Verlag, 2004, pp. 459–470.
Cantu-Paz, E. Designing Efficient Master-Slave Parallel Genetic Algorithms,
Genetic Programming 1998: Proceedings of the Third Annual Conference
, University of Wisconsin, 1998, pp. 455–463.
Gagné, C., Parizeau, M., Dubreuil, M. The Master-Slave Architecture for Evolutionary Computations Revisited,
Proceedings of Genetic and Evolutionary Computation
(GECCO 2003) Edited by Cantu-Paz, E. et al., Berlin, Springer Verlag, 2003, pp 1578–1579.
Dubreuil M. , '', in Proceedings of Genetic and Evolutionary Computation (GECCO 2003), (2003) -.
Dubreuil M. Proceedings of Genetic and Evolutionary Computation (GECCO 2003)2003)| false
Goldberg, D. E. Genetic
Algorithms in Search, Optimization, and Machine Learning
Addison-Wesley Publishing Company, Inc., 1989.
Whitley D. An overview of evolutionary algorithms: practical issues and common pitfalls,
Information and Software Technology
, 2001, pp. 817–831.
Marco-Blaszka N., Désidéri J. Numerical solution of optimization test-cases by genetic algorithms,
INRIA Research Report 3622