Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Andersen:2021:CEC,

• author = "Hayden Andersen and Andrew Lensen and Bing Xue",
• booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms",
• year = "2021",
• editor = "Yew-Soon Ong",
• pages = "688--695",
• address = "Krakow, Poland",
• month = "28 " # jun # "-1 " # jul,
• isbn13 = "978-1-7281-8393-0",
• abstract = "Clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality.",
• keywords = "genetic algorithms, genetic programming, Measurement, Clustering methods, Clustering algorithms, Evolutionary computation, Euclidean distance, Iterative methods, Clustering, Similarity Function, Feature Selection",
• DOI = "doi:10.1109/CEC45853.2021.9504855",
• notes = "Also known as \cite{9504855}",

}

Genetic Programming entries for Hayden Andersen Andrew Lensen Bing Xue

Citations