Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
Autonomic Optimization of an Oil Reservoir using Decentralized Services
CLADE '03 Proceedings of the 1st International Workshop on Challenges of Large Applications in Distributed Environments
The GRID: Computational and Data Resource Sharing in Engineering Optimisation and Design Search
ICPPW '01 Proceedings of the 2001 International Conference on Parallel Processing Workshops
Developing Services for Design Optimisation on the Grid
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
A scalable parallel genetic algorithm for x-ray spectroscopic analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Evolutionary algorithms-based parallel simulation-optimization framework for solving inverse problems
Future Generation Computer Systems
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This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid for computational tractability. The solution framework developed by the authors uses a masterâ聙聰worker strategy for task distribution and a pool for task mapping. Solution and computational performance results are presented for groundwater source identification and release history reconstruction problems. They indicate that high-quality solutions and significant raw performance improvements were attained for a deployment of the solution framework on the TeraGrid.