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Authors: Evgenii Sopov and Ilia Panfilov

Affiliation: Reshetnev Siberian State University of Science and Technology, Krasnoyarsk and Russia

ISBN: 978-989-758-380-3

Keyword(s): Hyperheuristics, Genetic Programming, Genetic Algorithm, Clustering, Electronic Component Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Hybrid Learning Systems ; Industrial Engineering ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Quality Control and Management ; Soft Computing

Abstract: A manufacture of electronic components involves the quality management, but still characteristics of components from different batches may vary. For many highly precise and reliable applications, such as aerospace or military systems, it is necessary to identify and use components from the same batch. This problem is usually stated as a clustering problem or as a k-centroids allocation problem. The k-centroids problem is a generalization of the Fermat–Weber location problem, which is known to be NP-hard. Genetic algorithms have proved their efficiency in solving many hard optimization problems. Genetic algorithms are also used in clustering algorithms for defining initial points of centroids for location-allocation clustering algorithms. At the same time, standard genetic algorithms demonstrates low performance in solving real-world clustering problems, and, as a result, different heuristic-based modifications have been proposed. In this study, we will synthesize a new selection heuri stic for a genetic algorithm, which is used for solving the clustering problem of identifying batches of electronic components. We will use a genetic programming based hyperheuristic for creating a selection operator represented by a probability distribution. The results of solving two real-world batch identification problems of microchip manufactures for aerospace applications are presented and are compared with base-line approaches and some previously obtained results. (More)

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Paper citation in several formats:
Sopov, E. and Panfilov, I. (2019). Genetic Programming based Synthesis of Clustering Algorithm for Identifying Batches of Electronic Components.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 202-209. DOI: 10.5220/0007810702020209

@conference{icinco19,
author={Evgenii Sopov. and Ilia Panfilov.},
title={Genetic Programming based Synthesis of Clustering Algorithm for Identifying Batches of Electronic Components},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007810702020209},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Genetic Programming based Synthesis of Clustering Algorithm for Identifying Batches of Electronic Components
SN - 978-989-758-380-3
AU - Sopov, E.
AU - Panfilov, I.
PY - 2019
SP - 202
EP - 209
DO - 10.5220/0007810702020209

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