A scalable cellular implementation of parallel genetic programming

A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The method adopts a load-balancing policy that avoids the unequal utilization of the processors. Experimental results on benchmark pro...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 7; no. 1; pp. 37 - 53
Main Authors: Folino, G., Pizzuti, C., Spezzano, G.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2003
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Concurrent computing
Degradation
Distributed computing
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Genetic programming
High performance computing
Parallel processing
Performance analysis
Scalability
Software
genetic programming (GP)
Parallel programming
Scalability
Genetic algorithm
Parallel processing
load balance
Performance analysis
Parallel computer
Load balancing
Cellular genetic programming model
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:A new parallel implementation of genetic programming (GP) based on the cellular model is presented and compared with both canonical GP and the island model approach. The method adopts a load-balancing policy that avoids the unequal utilization of the processors. Experimental results on benchmark problems of different complexity show the superiority of the cellular approach with respect to the canonical sequential implementation and the island model. A theoretical performance analysis reveals the high scalability of the implementation realized and allows to predict the size of the population when the number of processors and their efficiency are fixed.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2002.806168