Parallel genetic programming and its application to trading model induction
Parallel Computing
What's next in high-performance computing?
Communications of the ACM - Ontology: different ways of representing the same concept
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Parallel Processing of Adaptive Meshes with Load Balancing
IEEE Transactions on Parallel and Distributed Systems
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Dynamic Cluster Resource Allocations for Jobs with Known and Unknown Memory Demands
IEEE Transactions on Parallel and Distributed Systems
Parallel evolutionary training algorithms for “hardware-friendly“ neural networks
Natural Computing: an international journal
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
Parallel Single Front Genetic Algorithm: Performance Analysis in a Cluster System
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Strategies for optimizing image processing by genetic and evolutionary computation
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
(R) A Study of a Non-Linear Optimization Problem Using a Distributed Genetic Algorithm
ICPP '96 Proceedings of the Proceedings of the 1996 International Conference on Parallel Processing - Volume 2
Comparative evaluation of parallelization strategies for evolutionary and stochastic heuristics
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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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 load-bechmark. The implementation presents very satisfactory parallelization results, both in performance and scalability and Super-linear speedup is reached for several tests configurations. Experimental results show excellent perfomence, increasing the improvements with the load balancing algorithm.