Niching methods for genetic algorithms
Niching methods for genetic algorithms
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SETI@home: an experiment in public-resource computing
Communications of the ACM
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Monte Carlo Methods for Applied Scientists
Monte Carlo Methods for Applied Scientists
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Virtual Prairie: Going Green with Volunteer Computing
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Robust Asynchronous Optimization for Volunteer Computing Grids
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Evolutionary Algorithms (EA) have been extensively used in research to resolve optimization problems involving computationally intensive objective function evaluations. It is even more interesting to use a low-cost distributed computing platform based on Volunteer Computing (VC), to perform such optimizations. The downside is that VC compute nodes' volatility and unreliability associated with the level of task dependency introduced by parallel EA's tend to delay the algorithm's progress. This work proposes an enhanced scheduling of the BOINC (Berkeley Open Infrastructure for Network Computing) tasks associated with a Genetic Algorithm (GA) that aims at improving the performance of the algorithm. BOINC is the most popular middleware used for VC. While the GA has been chosen as it is the most commonly used EA, this approach is applicable to most of iterative EA's. The scheduling performs a matchmaking between a pool of tasks, classified according to their potential (predicted) fitness, and the pool of available hosts, classified according to their reliability. The scheduling technique have been implemented in a simulation environment and tested with benchmark functions. It proved to be effective in increasing the convergence speed and reducing the execution time of the GA.