Evolution-based scheduling of multiple variant and multiple processor programs
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
The influence of migration sizes and intervals on island models
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An analysis of island models in evolutionary computation
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
New upper bounds for the permutation flowshop scheduling problem
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Are multiple runs of genetic algorithms better than one?
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
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The problem of scheduling nonpreemtable tasks on parallel identical machines under constraint on discrete resource and requiring, additionally, renewable continuous resource to minimize the schedule length is considered in the paper. A continuous resource is divisible continuously and is allocated to tasks from given intervals in amounts unknown in advance. Task processing rate depends on the allocated amount of the continuous resource. To eliminate time-consuming optimal continuous resource allocation, a problem @Q"Z with continuous resource discretisation is introduced. Because @Q"Z is NP-hard a population-learning algorithm (PLA2) is proposed to tackle the problem. PLA2 is a population-based approach which takes advantage of the features common to the social education system rather than to the evolutionary processes. The proposed approach is based on the idea of constructing the hybrid algorithm integrating different optimization techniques complementing each other and producing a synergetic effect. Experimental results proved that PLA2 excels known algorithms for solving the considered problem.