Parallel genetic programming: a scalable implementation using the transputer network architecture
Advances in genetic programming
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Analysis of selection algorithms: A markov chain approach
Evolutionary Computation
Selection intensity in asynchronous cellular evolutionary algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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This paper presents a theoretical study about the selection pressure and the convergence speed of distributed evolutionary algorithms (dEA). In concrete, we model the best individual's growth curve and the takeover time for usual models of multipopulation EAs found in the literature. The calculation of the takeover time is a common analytical approach to measure the selection pressure of an EA. This work is another step forward to mathematically unify and describe the roles of all the parameters of the migration policy (the migration rate, the migration period, the topology, and the selection/replace schemes of immigrants) in the selection pressure induced by the dynamics of dEAs. In order to achieve these goals we analyze the behaviour of these algorithms and propose a mathematical formula which models that dynamic. The proposed mathematical model is later verified in practice.