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A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

A runtime analysis of parallel evolutionary algorithms in dynamic optimization
Authors: Andrei Lissovoi; Carsten Witt;

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

Abstract

A simple island model with λ islands and migration occurring after every τ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a ( 1 + λ ) EA if τ = 1 , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to λ = O ( n 1 - ϵ ) , the ( 1 + λ ) EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic λ . The relationship of τ , λ , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology.

Countries
United Kingdom, United Kingdom, Denmark
Keywords

island models, Applied Mathematics, Populations, Island models, Evolutionary algorithms, populations, Approximation methods and heuristics in mathematical programming, Article, Computer Science Applications, Dynamic problems, dynamic problems, Analysis of algorithms, Runtime analysis, Parallel algorithms in computer science, evolutionary algorithms, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), Computer Science(all), runtime analysis

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Top 10%
Top 10%
Green
hybrid