Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Parallel genetic algorithms for a hypercube
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming (videotape): the movie
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Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Parallel genetic programming and its application to trading model induction
Parallel Computing
Foundations of genetic programming
Foundations of genetic programming
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Papers from an international workshop on Towards Evolvable Hardware, The Evolutionary Engineering Approach
Placing and Routing Circuits on FPGAs by Means of Parallel and Distributed Genetic Programming
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Sizing Populations for Serial and Parallel Genetic Algorithms
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Experimental Study of Multipopulation Parallel Genetic Programming
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Parallel Distributed Genetic Programming Applied to the Evolution of Natural Language Recognisers
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GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
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Towards identifying populations that increase the likelihood of success in genetic programming
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Heterogeneous cooperative coevolution: strategies of integration between GP and GA
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Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
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Expert Systems with Applications: An International Journal
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BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
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A survey and taxonomy of performance improvement of canonical genetic programming
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EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
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Diversity in multipopulation genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
The effect of plagues in genetic programming: a study of variable-size populations
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A study of parallel and distributed particle swarm optimization methods
Proceedings of the 2nd workshop on Bio-inspired algorithms for distributed systems
Implicitly controlling bloat in genetic programming
IEEE Transactions on Evolutionary Computation
An empirical comparison of parallel and distributed particle swarm optimization methods
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On the impact of the migration topology on the Island Model
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IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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Dynamic size populations in distributed genetic programming
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Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
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Applied Soft Computing
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Natural Computing: an international journal
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This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we discuss the role of the parameters that characterize the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.