Genetic algorithms and classifier systems: foundations and future directions
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving toward an optimal scheduling solution through adaptivity
Journal of Parallel and Distributed Computing
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Locality Information Based Scheduling in Shared Memory Multiprocessors
IPPS '96 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Coevolution and Evolving Parallel Cellular Automata - Based Scheduling Algorithms
Selected Papers from the 5th European Conference on Artificial Evolution
Meta optimization: improving compiler heuristics with machine learning
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Adapting convergent scheduling using machine learning (citation_only)
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
Inducing heuristics to decide whether to schedule
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 1 - Volume 02
High-Performance Task Distribution for Volunteer Computing
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Predictor@Home: A "Protein Structure Prediction Supercomputer' Based on Global Computing
IEEE Transactions on Parallel and Distributed Systems
The Effectiveness of Threshold-Based Scheduling Policies in BOINC Projects
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
SimBA: A Discrete Event Simulator for Performance Prediction of Volunteer Computing Projects
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
Genetic programming heuristics for multiple machine scheduling
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Dynamic scheduling with genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Evolving accurate and compact classification rules with gene expression programming
IEEE Transactions on Evolutionary Computation
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Volunteer Computing (VC) is a paradigm that uses idle cycles from computing resources donated by volunteers and connected through the Internet to compute large-scale, loosely-coupled simulations. A big challenge in VC projects is the scheduling of work-units across heterogeneous, volatile, and error-prone computers. The design of effective scheduling policies for VC projects involves subjective and time-demanding tuning that is driven by the knowledge of the project designer. VC projects are in need of a faster and project-independent method to automate the scheduling design. To automatically generate a scheduling policy, we must explore the extremely large space of syntactically valid policies. Given the size of this search space, exhaustive search is not feasible. Thus in this paper we propose to solve the problem using an evolutionary method to automatically generate a set of scheduling policies that are project-independent, minimize errors, and maximize throughput in VC projects. Our method includes a genetic algorithm where the representation of individuals, the fitness function, and the genetic operators are specifically tailored to get effective policies in a short time. The effectiveness of our method is evaluated with SimBA, a Simulator of BOINC Applications. Contrary to manually-designed scheduling policies that often perform well only for the specific project they were designed for and require months of tuning, our resulting scheduling policies provide better overall throughput across the different VC projects considered in this work and were generated by our method in a time window of one week.