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| Presentation | 2019-01-24 15:00 A Genetic Algorithm-Based Method for Finding Approximate Solutions to Minimum Steiner Tree Problems Li-Ping Zhang, Norikazu Takahashi (Okayama Univ.), Zong-Xiao Yang (HAUST)  NLP2018-121 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The Euclidean Steiner Tree Problem (ESTP) is a classical combinatorial optimization problem that appears in various fields such as communication and transportation. Since the problem is NP-hard, the development of efficient approximation algorithms is needed. In this report, we propose a genetic algorithm-based method for finding an approximate solution to the ESTP and show its effectiveness by using four examples. Given a finite number of terminal points, the proposed algorithm first generates many non-terminal points by using a simple rule, and initializes the set of chromosomes, each of which corresponds to a part of the non-terminal points generated. It then runs a genetic algorithm to find a chromosome with the highest fitness value, where the fitness value of a chromosome is determined based on the length of the minimum spanning tree obtained from all terminal points and the non-terminal points corresponding to the chromosome. It finally optimizes the positions of non-terminal points in the minimum spanning tree. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | minimum Steiner tree / genetic algorithm / approximate solution / minimum spanning tree / combinatorial optimization / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 413, NLP2018-121, pp. 131-136, Jan. 2019. | |
| Paper # | NLP2018-121 | |
| Date of Issue | 2019-01-16 (NLP) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NLP2018-121 |
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| Conference Information | |
| Committee | NLP NC |
| Conference Date | 2019-01-23 - 2019-01-24 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | The Centennial Hall, Hokkaido Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | General Implementation of Neuro Computing, Analysis and Modeling of Human Science, etc. |
| Paper Information | |
| Registration To | NLP |
| Conference Code | 2019-01-NLP-NC |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Genetic Algorithm-Based Method for Finding Approximate Solutions to Minimum Steiner Tree Problems |
| Sub Title (in English) | |
| Keyword(1) | minimum Steiner tree |
| Keyword(2) | genetic algorithm |
| Keyword(3) | approximate solution |
| Keyword(4) | minimum spanning tree |
| Keyword(5) | combinatorial optimization |
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| 1st Author's Name | Li-Ping Zhang |
| 1st Author's Affiliation | Okayama University (Okayama Univ.) |
| 2nd Author's Name | Norikazu Takahashi |
| 2nd Author's Affiliation | Okayama University (Okayama Univ.) |
| 3rd Author's Name | Zong-Xiao Yang |
| 3rd Author's Affiliation | Henan University of Science and Technology (HAUST) |
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| Speaker | Author-1 |
| Date Time | 2019-01-24 15:00:00 |
| Presentation Time | 20 minutes |
| Registration for | NLP |
| Paper # | NLP2018-121 |
| Volume (vol) | vol.118 |
| Number (no) | no.413 |
| Page | pp.131-136 |
| #Pages | 6 |
| Date of Issue | 2019-01-16 (NLP) |
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