Experimental study on population-based incremental learning algorithms for dynamic optimization problems

Author:

Yang Shengxiang,Yao Xin

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Reference30 articles.

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2. Baker JE (1987) Reducing bias and inefficiency in the selection algorithms. In: Grefenstelle JJ (ed) Proceedings of the 2nd international conference on genetic algorithms. Lawrence Erlbaum Associates, pp 14-21

3. Baluja S (1994) Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical report CMU-CS-94-163, Carnegie Mellon University, USA

4. Baluja S, Caruana R (1995) Removing the genetics from the standard genetic algorithm. In: Proceedings of the 12th international conference on machine learning, pp 38–46

5. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 congress on evolutionary computation 3:1875–1882

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