Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning-Reference-Cited by-同舟云学术

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

Published:2017-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Virgolin Marco¹,Alderliesten Tanja²,Witteveen Cees³,Bosman Peter A. N.¹

Affiliation:

1. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

2. Academic Medical Center, Amsterdam, The Netherlands

3. Delft University of Technology, Delft, The Netherlands

Funder

Kinderen Kankervrij foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3071178.3071287

Reference18 articles.

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2. L. Breiman J. Friedman C. J. Stone and R. A. Olshen. 1984. Classification and regression trees. CRC press. L. Breiman J. Friedman C. J. Stone and R. A. Olshen. 1984. Classification and regression trees . CRC press.

3. Y. P. Chen T.-L. Yu K. Sastry and D. E. Goldberg. 2007. A survey of linkage learning techniques in genetic and evolutionary algorithms. IlliGAL report 2007014 (2007). Y. P. Chen T.-L. Yu K. Sastry and D. E. Goldberg. 2007. A survey of linkage learning techniques in genetic and evolutionary algorithms. IlliGAL report 2007014 (2007).

4. Evolving Binary Decision Diagrams using Implicit Neutrality

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