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Reference chromosome to overcome user fatigue in IEC

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Abstract

Evolutionary Computation encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of the genetic diversity, for which it is necessary to work with large populations. However, it is not always possible to deal with such large populations, for instance, when the adequacy values must be estimated by a human being (Interactive Evolutionary Computation, IEC). This work introduces a new algorithm which is able to perform very well with a very low number of individuals (micropopulations) which speeds up the convergence and it is solving problems with complex evaluation functions. The new algorithm is compared with the canonical genetic algorithm in order to validate its efficiency. Two experimental frameworks have been chosen: table and logotype designs. An objective evaluation measures has been proposed to avoid user interaction in the experiments. In both cases the results show the efficiency of the new algorithm in terms of quality of solutions and convergence speed, two key issues in decreasing user fatigue.

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Authors and Affiliations

  1. Universidad CARLOS III de Madrid y UPM, Campus de Leganés Avda. de la Universidad 30, 28911, Madrid, Spain

    Yago Saez, Pedro Isasi, Javier Segovia & Julio C. Hernandez

Authors
  1. Yago Saez
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  2. Pedro Isasi
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  3. Javier Segovia
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  4. Julio C. Hernandez
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Corresponding author

Correspondence to Yago Saez.

Additional information

Yago Saez: He received the Computer Engineering degree from the Universidad Pontificia de Salamanca in 1999 Spain. He now is a Ph.D. student and works as assistant professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas encompasses the interactive evolutionary computation, the design applications and the optimization problems.

Pedro Isasi, Ph.D.: He received Computer Science degree and Ph.D. degree from the Universidad Politécnica de Madrid (UPM), Spain in 1994. He is now working as professor at the EVANNAI Group at the Computer Science Department of CARLOS III, Madrid, Spain. His main research areas are Machine Learning, Evolutionary, Computation and Neural Networks and Applications to Optimization Problems.

Javier Segovia, Ph.D.: He is a receiving physicist, Ph.D. degree in Computer Science (with honours) from the Universidad Politécnica de Madrid (UPM). Currently Dean of the UPM School of Computer Science, and is editor and/or author of more than 70 scientific publications in the fields of genetic algorithms, data and web mining, artificial intelligence and intelligent interfaces.

Julio C. Hernandez, Ph.D.: He has received degree in Maths, Ph.D. degree in Computer Science. His main research area is the artificial intelligence applied to criptography and net security. His unofficial hobbies are chess and go. Currently, he is working as invited researcher at INRIA, France.

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Saez, Y., Isasi, P., Segovia, J. et al. Reference chromosome to overcome user fatigue in IEC. New Gener Comput 23, 129–142 (2005). https://doi.org/10.1007/BF03037490

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  • DOI: https://doi.org/10.1007/BF03037490

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