@inproceedings{96b06d318a594909a96b41879a67cbd6,
title = "Evolving to recognize high-dimensional relationships in data: GA operators and representation designed expressly for community detection",
abstract = "We present a new algorithm for network clustering based upon genetic algorithm methods to optimize modularity. The algorithm proposes an innovative, more abstract representation, along with newly designed domain-specific genetic operators. We then analyze the performance of the algorithm using popular real-world data sets taken from multiple domains. The analysis demonstrates that our algorithm consistently finds high quality or even optimal solutions without any a priori knowledge of the network or the desired number of clusters. Furthermore, we compare our results with five previously published methods and yield the highest quality for the largest of the benchmark datasets.",
keywords = "Clustering, Community detection, Genetic algorithm, Modularity",
author = "Kenneth Smith and Cezary Janikow and Sharlee Climer",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.; 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 ; Conference date: 13-07-2019 Through 17-07-2019",
year = "2019",
month = jul,
day = "13",
doi = "10.1145/3319619.3322073",
language = "English",
series = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
publisher = "Association for Computing Machinery, Inc",
pages = "308--309",
booktitle = "GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion",
}