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Author: Ulf Nieländer

Affiliation: Chemnitz University of Technology, Germany

Keyword(s): Genetic algorithms, Single-objective / Multi-objective optimization, CHEOPS, Omni Optimizer, Bench-marking, Test functions.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Evolutionary Multiobjective Optimization ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: This paper introduces the Chemnitz Hybrid Evolutionary Optimization System to the scientific community. CHEOPS is a non-standard, high-performance genetic algorithm framework allowing simple as well as advanced modes of operation. Universal genetic algorithms well-suited for solving both single- and multi-objective optimization problems are still a matter of serious research. The Omni Optimizer was a milestone in that research topic, but now it is dramatically outperformed by CHEOPS in single-objective optimization. The comparison should soon continue, because CHEOPS will be straightforwardly enhanced to solve multi-objective problems as well.

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Paper citation in several formats:
Nieländer, U. (2010). THE CHEMNITZ HYBRID EVOLUTIONARY OPTIMIZATION SYSTEM . In Proceedings of the International Conference on Evolutionary Computation (IJCCI 2010) - ICEC; ISBN 978-989-8425-31-7, SciTePress, pages 311-320. DOI: 10.5220/0003059203110320

@conference{icec10,
author={Ulf Nieländer.},
title={THE CHEMNITZ HYBRID EVOLUTIONARY OPTIMIZATION SYSTEM },
booktitle={Proceedings of the International Conference on Evolutionary Computation (IJCCI 2010) - ICEC},
year={2010},
pages={311-320},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003059203110320},
isbn={978-989-8425-31-7},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation (IJCCI 2010) - ICEC
TI - THE CHEMNITZ HYBRID EVOLUTIONARY OPTIMIZATION SYSTEM
SN - 978-989-8425-31-7
AU - Nieländer, U.
PY - 2010
SP - 311
EP - 320
DO - 10.5220/0003059203110320
PB - SciTePress

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