Towards high speed multiobjective evolutionary optimizers
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An unorthodox introduction to Memetic Algorithms
ACM SIGEVOlution
A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization
Genetic Programming and Evolvable Machines
Evolutionary multi-objective optimization on grid environments
PDCN '08 Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks
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This paper analyzes some technical and practical issues concerning the use of parallel systems to solve multi-objective optimization problems using enumerative search. This technique constitutes a conceptually simple search strategy, and it is based on evaluating each possible solution from a given finite search space. The results obtained by enumeration are impractical for most computer platforms and researchers, but they exhibit a great interest because they can be used to be compared against the values obtained by stochastic techniques. We analyze here the use of a grid computing system to cope with the limits of enumerative search. After evaluating the performance of the sequential algorithm, we present, first, a parallel algorithm targeted to multiprocessor systems. Then, we design a distributed version prepared to be executed on a federation of geographically distributed computers known as a computational grid. Our conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.