Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem-Reference-Cited by-同舟云学术

Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Przewozniczek Michal W.¹,Komarnicki Marcin M.¹

Affiliation:

1. Wroclaw Univ. of Science and Techn., Wroclaw, Poland

Funder

Polish National Science Centre

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520304.3534059

Reference12 articles.

1. Brian W. Goldman and William F. Punch. 2014. Parameter-less Population Pyramid . In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation ( Vancouver, BC, Canada) (GECCO '14). 785--792. Brian W. Goldman and William F. Punch. 2014. Parameter-less Population Pyramid. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (Vancouver, BC, Canada) (GECCO '14). 785--792.

2. Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing

3. Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme

4. DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization

5. Active Multi-Population Pattern Searching Algorithm for flow optimization in computer networks – The novel coevolution schema combined with linkage learning

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