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Authors: Jörg Bremer and Michael Sonnenschein

Affiliation: University of Oldenburg, Germany

Keyword(s): Smart Grid, Soft Computing, Evolutionary Optimization, Constraint Modeling, SVDD.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Enterprise Information Systems ; Formal Methods ; Hybrid Intelligent Systems ; Industrial Applications of AI ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Planning and Scheduling ; Simulation and Modeling ; Soft Computing ; Symbolic Systems

Abstract: A new application for support vector machines is their use for meta-modeling feasible regions in constrained optimization problems. We here describe a solution for the still unsolved problem of a standardized integration of such models into (evolutionary) optimization algorithms with the help of a new decoder based approach. This goal is achieved by constructing a mapping function that maps the whole unconstrained domain of a given problem to the region of feasible solutions with the help of the the support vector model. The applicability to real world problems is demonstrated using the load balancing problem from the smart grid domain.

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Paper citation in several formats:
Bremer, J. and Sonnenschein, M. (2013). Constraint-handling for Optimization with Support Vector Surrogate Models - A Novel Decoder Approach. In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8565-39-6; ISSN 2184-433X, SciTePress, pages 91-100. DOI: 10.5220/0004241100910100

@conference{icaart13,
author={Jörg Bremer. and Michael Sonnenschein.},
title={Constraint-handling for Optimization with Support Vector Surrogate Models - A Novel Decoder Approach},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2013},
pages={91-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004241100910100},
isbn={978-989-8565-39-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Constraint-handling for Optimization with Support Vector Surrogate Models - A Novel Decoder Approach
SN - 978-989-8565-39-6
IS - 2184-433X
AU - Bremer, J.
AU - Sonnenschein, M.
PY - 2013
SP - 91
EP - 100
DO - 10.5220/0004241100910100
PB - SciTePress

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