Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?

Author:

Muelas Santiago,Mendiburu Alexander,LaTorre Antonio,Peña José-María

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference43 articles.

1. Chang W. Ahn, David E. Goldberg, R.S. Ramakrishna, Multiple-deme parallel Estimation of Distribution Algorithms: basic framework and application, in: Parallel Processing and Applied Mathematics, 2004, pp. 544–551.

2. Parallel evolutionary algorithms can achieve super-linear performance;Alba;Information Processing Letters,2002

3. Influence of the migration policy in parallel distributed gas with structured and panmictic populations;Alba;Applied Intelligence,2000

4. Improving flexibility and efficiency by adding parallelism to genetic algorithms;Alba;Statistics and Computing,2002

5. S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical report, Carnegie Mellon University, 1994.

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