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Authors: Ayman Elkasaby ; Akram Salah and Ehab Elfeky

Affiliation: Cairo University, Egypt

ISBN: 978-989-758-218-9

Keyword(s): Genetic Programming, Multiobjective Optimization, Epsilon Dominance, Evolutionary Algorithms.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Methodologies and Technologies ; Operational Research ; Optimization ; Stochastic Optimization ; Symbolic Systems

Abstract: Multi-objective optimization is currently an active area of research, due to the difficulty of obtaining diverse and high-quality solutions quickly. Focusing on the diversity or quality aspect means deterioration of the other, while optimizing both results in impractically long computational times. This gives rise to approximate measures, which relax the constraints and manage to obtain good-enough results in suitable running times. One such measure, epsilon-dominance, relaxes the criteria by which a solution dominates another. Combining this measure with genetic programming, an evolutionary algorithm that is flexible and can solve sophisticated problems, makes it potentially useful in solving difficult optimization problems. Preliminary results on small problems prove the efficacy of the method and suggest its potential on problems with more objectives.

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Paper citation in several formats:
Elkasaby, A.; Salah, A. and Elfeky, E. (2017). Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance.In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 424-429. DOI: 10.5220/0006219504240429

@conference{icores17,
author={Ayman Elkasaby. and Akram Salah. and Ehab Elfeky.},
title={Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2017},
pages={424-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006219504240429},
isbn={978-989-758-218-9},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Multiobjective Optimization using Genetic Programming: Reducing Selection Pressure by Approximate Dominance
SN - 978-989-758-218-9
AU - Elkasaby, A.
AU - Salah, A.
AU - Elfeky, E.
PY - 2017
SP - 424
EP - 429
DO - 10.5220/0006219504240429

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