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Deep memetic models for combinatorial optimization problems: application to the tool switching problem

  • Jhon Edgar Amaya
  • Carlos Cotta
  • Antonio J. Fernández-Leiva
  • Pablo García-SánchezEmail author
Regular Research Paper

Abstract

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature.

Keywords

Deep architecture Hybrid algorithms Memetic algorihms Tool switching problem (ToSP) 

Notes

Acknowledgements

The authors wish to thank the anonymous reviewers for their helpful comments. The first author thanks to the Decanato de Investigación of UNET the partial support of the present research. Second and third author were partially supported by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech, and also by research projects Ephemech (https://ephemech.wordpress.com/) (TIN2014-56494-C4-1-P), and DeepBio (https://deepbio.wordpress.com) (TIN2017-85727-C4-01-P), funded by Ministerio Español de Economía y Competitividad. Fourth author was also partially supported by “Ayuda del Programa de Fomento e Impulso de la Actividad Investigadora de la Universidad de Cádiz”.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Universidad Nacional Experimental del Táchira (UNET), Laboratorio de Computación de Alto Rendimiento (LCAR)San CristóbalVenezuela
  2. 2.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaUniversity of MalagaMalagaSpain
  3. 3.Dept. de Ingeniería Informática, ESIUniversity of CádizCádizSpain

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