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Using Novelty Search in Differential Evolution

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Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection (PAAMS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 887))

Abstract

Novelty search in evolutionary robotics measures a distance of potential novelty solutions to their k-nearest neighbors in the search space. This distance presents an additional objective to the fitness function, with which each individual in population is evaluated. In this study, the novelty search was applied within the differential evolution. The preliminary results on CEC-14 Benchmark function suite show its potential for using also in the future.

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References

  1. Eiben, A.E., Smith, J.E.: From evolutionary computation to the evolution of things. Nature 521(7553), 476–482 (2015)

    Article  Google Scholar 

  2. Nelson, A.L.: Embodied artificial life at an impasse can evolutionary robotics methods be scaled? In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), Orlando, FL, pp. 25–34 (2014)

    Google Scholar 

  3. Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of the Eleventh International Conference on Artificial Life (ALIFE XI), pp. 329–336. MIT Press, Cambridge (2008)

    Google Scholar 

  4. Gomes, J., Mariano, P., Christensen, A.L.: Devising effective novelty search algorithms: a comprehensive empirical study. In: Silva, S. (ed.) Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO 2015), pp. 943–950. ACM, New York (2015)

    Google Scholar 

  5. Doncieux, S., Mouret, J.B.: Behavioral diversity measures for evolutionary robotics. In: IEEE Congress on Evolutionary Computation, Barcelona, pp. 1–8 (2010)

    Google Scholar 

  6. Doncieux, S., Mouret, J.B.: Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evol. Intell. 7(2), 71–93 (2014)

    Article  Google Scholar 

  7. Lynch, M.: The evolution of genetic networks by non-adaptive processes. Nat. Rev. Genet. 8, 803–813 (2007)

    Article  Google Scholar 

  8. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  9. Gomes, J., Mariano, P., Christensen, A.L.: Avoiding convergence in cooperative coevolution with novelty search. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2014), pp. 1149–1156. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2014)

    Google Scholar 

  10. Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19, 189–223 (2011)

    Article  Google Scholar 

  11. Liapis, A., Yannakakis, G.N., Togelius, J.: Constrained novelty search: a study on game content generation. Evol. Comput. 23, 101–129 (2015)

    Article  Google Scholar 

  12. Standish, R.K.: Open-ended artificial evolution. Int. J. Comput. Intell. Appl. 3(2), 167–175 (2003)

    Article  Google Scholar 

  13. Naredo, E., Trujillo, L.: Searching for novel clustering programs. In: Blum, C. (ed.) Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013), pp. 1093–1100. ACM, New York (2013)

    Google Scholar 

  14. Storn, R., Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  15. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  16. Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: IEEE Congress on Evolutionary Computation (CEC), 2014, Beijing, pp. 1658–1665 (2014)

    Google Scholar 

  17. Erlich, I., Rueda, J.L., Wildenhues, S., Shewarega, F.: Evaluating the mean-variance mapping optimization on the IEEE-CEC 2014 test suite. In: 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, pp. 1625–1632 (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, Maribor, Slovenia

    Iztok Fister & Iztok Fister Jr.

  2. Toho University, 2-2-1 Miyama, Funabashi, 274-8510, Japan

    Andres Iglesias & Akemi Galvez

  3. University of Cantabria, Avenida de los Castros, s/n, 39005, Santander, Spain

    Iztok Fister, Andres Iglesias & Akemi Galvez

  4. TECNALIA, Derio, Spain

    Javier Del Ser & Eneko Osaba

  5. University of the Basque Country (UPV/EHU), Bilbao, Spain

    Javier Del Ser

  6. Basque Center for Applied Mathematics (BCAM), Bilbao, Spain

    Javier Del Ser

Authors
  1. Iztok Fister
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  2. Andres Iglesias
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  3. Akemi Galvez
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  4. Javier Del Ser
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  5. Eneko Osaba
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  6. Iztok Fister Jr.
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Corresponding author

Correspondence to Iztok Fister .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Javier Bajo

  2. Departamento de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Juan M. Corchado

  3. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Elena María Navarro Martínez

  4. TECNALIA, Derio, Spain

    Eneko Osaba Icedo

  5. Lille University of Science and Technology, Villeneuve d’Ascq, France

    Philippe Mathieu

  6. Poznan University of Technology, Poznan, Poland

    Patrycja Hoffa-DÄ…browska

  7. Polytechnic University of Valencia, Valencia, Spain

    Elena del Val

  8. Faculté des Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Sylvain Giroux

  9. University of Porto, Porto, Portugal

    Antonio J.M. Castro

  10. Rio de Janeiro State University, Rio de Janeiro, Brazil

    Nayat Sánchez-Pi

  11. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julián

  12. Informatica e Estatistica, Universidade Federal de Santa Catar, Florianopolis, Santa Catarina, Brazil

    Ricardo Azambuja Silveira

  13. Universidad Rey Juan Carlos, Móstoles, Madrid, Spain

    Alberto Fernández

  14. ICB, Universität Duisburg-Essen, Essen, Germany

    Rainer Unland

  15. Facultad de Informática, Universidad Complutense de Madrid, Madrid, Spain

    Rubén Fuentes-Fernández

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Fister, I., Iglesias, A., Galvez, A., Del Ser, J., Osaba, E., Fister, I. (2018). Using Novelty Search in Differential Evolution. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_46

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  • DOI: https://doi.org/10.1007/978-3-319-94779-2_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94778-5

  • Online ISBN: 978-3-319-94779-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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