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Increasing the Throughput of Expensive Evaluations Through a Vector Based Genetic Programming Framework

Published:20 July 2016Publication History

ABSTRACT

Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators [6]. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions.

A challenge in this field is working with both large datasets and expensive primitive functions. This paper outlines some of the innovations Zutty et al. have introduced into the GTMOEP framework in order to more efficiently evaluate individuals and tackle new problems. These innovations include: Working with non-feature data, tiered datasets, subtree caches, and initial population creation.

References

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  3. K. Holladay and K. Robbins. Evolution of signal processing algorithms using vector based genetic programming. In Digital Signal Processing, 2007 15th International Conference on, pages 503--506. IEEE, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  4. G. Rohling. Multiple objective evolutionary algorithms for independent, computationally expensive objective evaluations. PhD thesis, Georgia Institute of Technology, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Streater. Genetic programming for the automatic construction of features in skin-lesion image classification. Master's thesis, University of Edinburgh, 2010.Google ScholarGoogle Scholar
  6. J. Zutty, D. Long, H. Adams, G. Bennett, and C. Baxter. Multiple objective vector-based genetic programming using human-derived primitives. In Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, pages 1127--1134. ACM, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Increasing the Throughput of Expensive Evaluations Through a Vector Based Genetic Programming Framework

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          cover image ACM Conferences
          GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
          July 2016
          1510 pages
          ISBN:9781450343237
          DOI:10.1145/2908961

          Copyright © 2016 Owner/Author

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 20 July 2016

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          GECCO '16 Companion Paper Acceptance Rate137of381submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

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