Reducing bias and inefficiency in the selection algorithm
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The design and analysis of parallel algorithms
The design and analysis of parallel algorithms
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Digital neural networks
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
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Parallel Genetic Algorithms: Theory and Applications
Parallel Genetic Algorithms: Theory and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
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Analyzing synchronous and asynchronous parallel distributed genetic algorithms
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A New Interpretation of Schema Notation that Overtums the Binary Encoding Constraint
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
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Proceedings of the 5th International Conference on Genetic Algorithms
A Fine-Grained Parallel Genetic Algorithm for Distributed Parallel Systems
Proceedings of the 5th International Conference on Genetic Algorithms
On Decentralizing Selection Algorithms
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Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm
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Genetic Algorithms for Protocol Validation
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An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
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Noise Prediction in Urban Traffic by a Neural Approach
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Type-constrained genetic programming for rule-base definition in fuzzy logic controllers
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Genetic drift in genetic algorithm selection schemes
IEEE Transactions on Evolutionary Computation
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Journal of Parallel and Distributed Computing
Strong definitions of performance metrics
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Adaptive cellular memetic algorithms
Evolutionary Computation
Modelling and Simulation in Engineering
A Solution Framework for Environmental Characterization Problems
International Journal of High Performance Computing Applications
Differential evolution algorithms with cellular populations
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Theory and practice of cellular UMDA for discrete optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On parallel immune quantum evolutionary algorithm based on learning mechanism and its convergence
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Cellular genetic algorithms without additional parameters
The Journal of Supercomputing
Asynchronous master-slave parallelization of differential evolution for multi-objective optimization
Evolutionary Computation
Information Sciences: an International Journal
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In this paper we develop a study on several types of parallel genetic algorithms (PGAs). Our motivation is to bring some uniformity to the proposal, comparison, and knowledge exchange among the traditionally opposite kinds of serial and parallel GAs. We comparatively analyze the properties of steady-state, generational, and cellular genetic algorithms. Afterwards, this study is extended to consider a distributed model consisting in a ring of GA islands. The analyzed features are the time complexity, selection pressure, schema processing rates, efficacy in finding an optimum, efficiency, speedup, and resistance to scalability. Besides that, we briefly discuss how the migration policy affects the search. Also, some of the search properties of cellular GAs are investigated. The selected benchmark is a representative subset of problems containing real world difficulties. We often conclude that parallel GAs are numerically better and faster than equivalent sequential GAs. Our aim is to shed some light on the advantages and drawbacks of various sequential and parallel GAs to help researchers using them in the very diverse application fields of the evolutionary computation.