A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach

Jörg Bremer, Sebastian Lehnhoff

Abstract

Evolution strategies have been successfully applied to optimization problems with rugged, multi-modal fitness landscapes, to non linear problems, and to derivative free optimization. Usually evolution is performed by exploiting the structure of the objective function. In this paper, we present an approach that harnesses the adapting quantum potential field determined by the spatial distribution of elitist solutions as guidance for the next generation. The potential field evolves to a smoother surface leveling local optima but keeping the global structure what in turn allows for a faster convergence of the solution set. We demonstrate the applicability and the competitiveness of our approach compared with particle swarm optimization and the well established evolution strategy CMA-ES.

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  35. Used test functions (Ulmer et al., 2003; Ahrari and Shariat-Panahi, 2013; Himmelblau, 1972; Yao et al., 1999; Mishra, 2006). 2
  36. f9pxq “ x1 12x1 11 10 cosppx1{2q 8 sinp5px1q p1{5q0.5é0.5px20.5q2 , 30 d x1, x2 d 30 with x° “ p5.90133, 0.5q f9px°q “ 43.3159.
  37. f10pxq “ px1 2x2 7q2p2x1 x2 5q2, 20 d x1, x2 d 20 with x° “ p1, 3q f9px°q “ 0.
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Paper Citation


in Harvard Style

Bremer J. and Lehnhoff S. (2016). A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 21-29. DOI: 10.5220/0006037000210029


in Bibtex Style

@conference{ecta16,
author={Jörg Bremer and Sebastian Lehnhoff},
title={A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={21-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006037000210029},
isbn={978-989-758-201-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - A Quantum Field Evolution Strategy - An Adaptive Surrogate Approach
SN - 978-989-758-201-1
AU - Bremer J.
AU - Lehnhoff S.
PY - 2016
SP - 21
EP - 29
DO - 10.5220/0006037000210029