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Genetic Programming and Evolvable Machines

, Volume 19, Issue 3, pp 351–389 | Cite as

Unveiling evolutionary algorithm representation with DU maps

  • Eric Medvet
  • Marco Virgolin
  • Mauro Castelli
  • Peter A. N. Bosman
  • Ivo Gonçalves
  • Tea Tušar
Article
Part of the following topical collections:
  1. Genetic Programming, Evolutionary Computation and Visualization

Abstract

Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analyzing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use.

Keywords

Representation Diversity Usage GE WHGE SGE GSGP GOMEA NEAT Visualization Heat maps 

Notes

Acknowledgements

This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 692286. This work was also financed through the Regional Operational Programme CENTRO2020 within the scope of the Project CENTRO-01-0145-FEDER-000006. Marco Virgolin received financial support from the Kinderen Kankervrij foundation (Project No. 187).

Supplementary material

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Supplementary material 1 (pdf 1685 KB)

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Authors and Affiliations

  1. 1.Centrum Wiskunde & InformaticaAmsterdamThe Netherlands
  2. 2.NOVA Information Management School (NOVA IMS)Universidade Nova de LisboaLisbonPortugal
  3. 3.INESC Coimbra, DEECUniversity of CoimbraCoimbraPortugal
  4. 4.Department of Engineering and ArchitectureUniversity of TriesteTriesteItaly
  5. 5.Department of Intelligent SystemsJoŭef Stefan InstituteLjubljanaSlovenia

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