Design and analysis of migration in parallel evolutionary algorithms

Parallelization is becoming a more important issue for solving difficult optimization problems. Island models combine phases of independent evolution with migration where genetic information is spread out to neighbored islands. This can lead to an increased diversity within the population. Despite m...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 17; H. 7; S. 1121 - 1144
Hauptverfasser: Lässig, Jörg, Sudholt, Dirk
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer-Verlag 01.07.2013
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Communication
Computational Intelligence
Control
Empirical analysis
Engineering
Evolutionary algorithms
Focus
Genetic algorithms
Islands
Mathematical Logic and Foundations
Mechatronics
Parallel processing
Polynomials
Robotics
Statistical analysis
Statistical tests
Topology
Spatial structures
Island model
Runtime analysis
Migration
Distributed evolutionary algorithms
Parallel evolutionary algorithms
Multi-deme model
ISSN:1432-7643, 1433-7479
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Zusammenfassung:Parallelization is becoming a more important issue for solving difficult optimization problems. Island models combine phases of independent evolution with migration where genetic information is spread out to neighbored islands. This can lead to an increased diversity within the population. Despite many successful applications, the reasons behind the success of island models are not well understood. We perform a first rigorous runtime analysis for island models and construct a function where phases of independent evolution as well as communication among the islands are essential. A simple island model with migration finds a global optimum in polynomial time, while panmictic populations as well as island models without migration need exponential time, with very high probability. Our results lead to new insights into the usefulness of migration, how information is propagated in island models, and how to set parameters such as the migration interval. This is a novel contribution to the theoretical foundation of parallel EAs. Further, we provide empirical results that complement the theoretical results, investigate the robustness with respect to the choice of the migration interval and compare various migration topologies using statistical tests.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-013-0991-0