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Adaptive Integration Using Evolutionary Strategies
 In Proceedings of the International Conference on High Performance Computing (HiPC '96
, 1996
"... Multivariate integration problems arising in the real world often lead to computationally intensive numerical solutions. If the singularities and/or peaks in the integrand are not known a priori, the use of adaptive methods is recommended. The efficiency of adaptive methods depends heavily on focusi ..."
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Multivariate integration problems arising in the real world often lead to computationally intensive numerical solutions. If the singularities and/or peaks in the integrand are not known a priori, the use of adaptive methods is recommended. The efficiency of adaptive methods depends heavily on focusing on the subregions that contain singularities or peaks in the integrands. In this paper, we present techniques based on evolutionary strategies that can be used to identify such subregions. Adaptive integration algorithms and evolutionary strategies can be parallelized easily and hence combining the parallel implementations of these result in efficient parallel adaptive integration algorithms. 1. Introduction An investigation of fast techniques for numerical integration is motivated by the need to compute computationally intensive multiple integrals arising in various areas of science and engineering, for example in finite element applications. The objective is to compute an approximatio...
Parallel Implementation of Evolutionary Strategies on Heterogeneous Clusters with Load Balancing ∗
"... This paper presents a load balancing algorithm for a parallel implementation of an evolutionary strategy on heterogeneous clusters. Evolutionary strategies can efficiency solve a diverse set of optimization problems. Due to cluster heterogeneity and in order to improve the speedup of the parallel im ..."
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This paper presents a load balancing algorithm for a parallel implementation of an evolutionary strategy on heterogeneous clusters. Evolutionary strategies can efficiency solve a diverse set of optimization problems. Due to cluster heterogeneity and in order to improve the speedup of the parallel implementation a load balancing algorithm has been implemented. This load balancing algorithm takes into account cluster heterogeneity and it is based on an optimal intial distribution. This initial distribution is determined based on the cluster nodes ’ computational powers, that are dinamically measured in each slave node by an ad hoc loadbechmark. The implementation presents very satisfactory parallelization results, both in performance and scalability and Superlinear speedup is reached for several tests configurations. Experimental results show excellent perfomence, increasing the improvements with the load balancing algorithm. 1.