Differential Evolution and Deterministic Chaotic Series: A Detailed Study

Roman Senkerik; Adam Viktorin; Ivan Zelinka; Michal Pluhacek; Tomas Kadavy; Zuzana Kominkova Oplatkova; Vikrant Bhateja; Suresh Chandra Satapathy

doi:10.13164/mendel.2018.2.061

Roman Senkerik Tomas Bata University in Zlin, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence
Adam Viktorin Tomas Bata University in Zlin, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence
Ivan Zelinka VŠB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Computer Science
Michal Pluhacek Tomas Bata University in Zlin, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence
Tomas Kadavy Tomas Bata University in Zlin, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence
Zuzana Kominkova Oplatkova Tomas Bata University in Zlin, Faculty of Applied Informatics, Department of Informatics and Artificial Intelligence
Vikrant Bhateja Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Department of Electronics and Communication Engineering
Suresh Chandra Satapathy Kalinga Institute of Industrial Technology, School of Computer Engineering

DOI: https://doi.org/10.13164/mendel.2018.2.061

Keywords: Differential Evolution, Complex dynamics, Deterministic chaos, Population diversity, Chaotic map

Abstract

This research represents a detailed insight into the modern and popular hybridization of deterministic chaotic dynamics and evolutionary computation. It is aimed at the influence of chaotic sequences on the performance of four selected Differential Evolution (DE) variants. The variants of interest were: original DE/Rand/1/ and DE/Best/1/ mutation schemes, simple parameter adaptive jDE, and the recent state of the art version SHADE. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in DE algorithm driven by the nine different two-dimensional discrete deterministic chaotic systems, as the chaotic pseudorandom number generators. The performances of DE variants and their chaotic/non-chaotic versions are recorded in the one-dimensional settings of 10D and 15 test functions from the CEC 2015 benchmark, further statistically analyzed.

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