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On Interactive Evolution Strategies

  • Ron Breukelaar
  • Michael Emmerich
  • Thomas Bäck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

In this paper we discuss Evolution Strategies within the context of interactive optimization. Different modes of interaction will be classified and compared. A focus will be on the suitability of the approach in cases, where the selection of individuals is done by a human user based on subjective evaluation. We compare the convergence dynamics of different approaches and discuss typical patterns of user interactions observed in empirical studies.

The discussion of empirical results will be based on a survey conducted via the world wide web. A color (pattern) redesign problems from literature will be adopted and extended. The simplicity of the chosen problems allowed us to let a larger number of people participate in our study. The amount of data collected makes it possible to add statistical support to our hypothesis about the performance and behavior of different Interactive Evolution Strategies and to figure out high-performing instantiations of the approach.

The behavior of the user was also compared to a deterministic selection of the best individual by the computer. This allowed us to figure out how much the convergence speed is affected by noise and to estimate the potential for accelerating the algorithm by means of advanced user interaction schemes.

Keywords

Convergence Speed Color Pattern Target Color Ideal User Computer Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ron Breukelaar
    • 1
  • Michael Emmerich
    • 1
  • Thomas Bäck
    • 1
    • 2
  1. 1.Natural Computing GroupUniversity of LeidenLeidenThe Netherlands
  2. 2.NuTech Solutions GmbHDortmundGermany

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