TY - GEN
T1 - A Genetic Algorithm for Finding Regular Graphs with Minimum Average Shortest Path Length
AU - Hayashi, Reiji
AU - Migita, Tsuyoshi
AU - Takahashi, Norikazu
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - The problem of finding a simple regular graph with the specified order and degree that minimizes the average shortest path length has a long history in graph theory. Recently this problem has attracted a great deal of attention in relation to the design of computer networks in data centers. In this paper, we propose a genetic algorithm for finding an approximate solution to this problem. Because the search space is the set of all simple regular graphs with the specified order and degree, conventional genetic algorithms cannot be directly applied. We propose in this paper new crossover and mutation operators that guarantee the simplicity and regularity of graphs. We also evaluate the effectiveness of the proposed method experimentally.
AB - The problem of finding a simple regular graph with the specified order and degree that minimizes the average shortest path length has a long history in graph theory. Recently this problem has attracted a great deal of attention in relation to the design of computer networks in data centers. In this paper, we propose a genetic algorithm for finding an approximate solution to this problem. Because the search space is the set of all simple regular graphs with the specified order and degree, conventional genetic algorithms cannot be directly applied. We propose in this paper new crossover and mutation operators that guarantee the simplicity and regularity of graphs. We also evaluate the effectiveness of the proposed method experimentally.
KW - crossover
KW - generalized Moore graph
KW - genetic algorithm
KW - mutation
KW - regular graph
UR - http://www.scopus.com/inward/record.url?scp=85099682106&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099682106&partnerID=8YFLogxK
U2 - 10.1109/SSCI47803.2020.9308205
DO - 10.1109/SSCI47803.2020.9308205
M3 - Conference contribution
AN - SCOPUS:85099682106
T3 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
SP - 2431
EP - 2436
BT - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Y2 - 1 December 2020 through 4 December 2020
ER -