A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A parallel genetic algorithm to solve the set-covering problem
Computers and Operations Research
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
CAGE: A Tool for Parallel Genetic Programming Applications
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
VECPAR '00 Selected Papers and Invited Talks from the 4th International Conference on Vector and Parallel Processing
Application of a Genetic Programming Based Rule Discovery System to Recurring Miscarriage Data
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Hierarchical task topology for retrieving information from within a simulated information ecosystem
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Voronoi-initializated island models for solving real-coded deceptive problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
High-performance, parallel, stack-based genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Parallel genetic programming: an application to trading models evolution
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Solving job shop scheduling problem using a hybrid parallel micro genetic algorithm
Applied Soft Computing
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This report describes the parallel implementation of genetic programming in the C programming language using a PC 486 type computer (running Windows) acting as a host and a network of transputers acting as processing nodes. Using this approach, researchers of genetic algorithms and genetic programming can acquire computing power that is intermediate between the power of currently available workstations and that of supercomputers at a cost that is intermediate between the two. A comparison is made of the computational effort required to solve the problem of symbolic regression of the Boolean even-5-parity function with different migration rates. Genetic programming required the least computational effort with an 8% migration rate. Moreover, this computational effort was less than that required for solving the problem with a serial computer and a panmictic population of the same size. That is, apart from the nearly linear speed-up in executing a fixed amount of code inherent in the parallel implementation of genetic programming, parallelization delivered more than linear speed-up in solving the problem using genetic programming.