PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
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IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
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Proceedings of the 10th annual conference on Genetic and evolutionary computation
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ARCS '09 Proceedings of the 22nd International Conference on Architecture of Computing Systems
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IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
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IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dynamic search initialisation strategies for multi-objective optimisation in peer-to-peer networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
The parallel single front genetic algorithm (PSFGA) in dynamic multi-objective optimization
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
An investigation on noise-induced features in robust evolutionary multi-objective optimization
Expert Systems with Applications: An International Journal
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International Journal of High Performance Systems Architecture
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Applied Soft Computing
Parallel implementation of evolutionary strategies on heterogeneous clusters with load balancing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Parallelism on multicore processors using Parallel.FX
Advances in Engineering Software
Particle swarm approach for parameter optimization of quantum well nano structure
Expert Systems with Applications: An International Journal
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KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Comparison of parallel multi-objective approaches to protein structure prediction
The Journal of Supercomputing
Generic parallel genetic algorithm framework for protein optimisation
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part II
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Using computational intelligence and parallelism to solve an industrial design problem
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
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AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
Evolutionary Computation
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Computational Mechanics
Journal of Global Optimization
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Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEA's effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEA's parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this paper's analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA.