Parallel genetic algorithms for a hypercube
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
The royal tree problem, a benchmark for single and multiple population genetic programming
Advances in genetic programming
Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming
Genetic Programming and Evolvable Machines
Migration Policies, Selection Pressure, and Parallel Evolutionary Algorithms
Journal of Heuristics
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Computer simulations of genetic adaptation: parallel subcomponent interaction in a multilocus model
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Investigating vesicular selection
Applied Soft Computing
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Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the test problem, this paper primarily investigates the influences of some important parallel control parameters within parallel genetic programming (GP), including the degree of connectivity between demes, the migration rate, the migration generation interval, and the migration policy, on the performance of the parallel evolutionary modeling algorithm (PEMA), which is measured from two perspectives: the solution quality and the parallel speedup. We compare the results with previous theoretical and experimental work in parallel genetic algorithms (GAs), and try to give some plausible analysis and explanations. The results may help to offer some useful design guidelines for researchers using parallel GP.