M3GP – multiclass classification with GP

Luis Muñoz, Leonardo Trujillo, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

31 Citations (Scopus)


Data classification is one of the most ubiquitous machine learning tasks in science and engineering. However, Genetic Programming is still not a popular classification methodology, partially due to its poor performance in multiclass problems. The recently proposed M2GP - Multidimensional Multiclass Genetic Programming algorithm achieved promising results in this area, by evolving mappings of the p-dimensional data into a d-dimensional space, and applying a minimum Mahalanobis distance classifier. Despite good performance, M2GP employs a greedy strategy to set the number of dimensions d for the transformed data, and fixes it at the start of the search, an approach that is prone to locally optimal solutions. This work presents the M3GP algorithm, that stands for M2GP with multidimensional populations. M3GP extends M2GP by allowing the search process to progressively search for the optimal number of new dimensions d that maximize the classification accuracy. Experimental results show that M3GP can automatically determine a good value for d depending on the problem, and achieves excellent performance when compared to state-of-the-art-methods like Random Forests, Random Subspaces and Multilayer Perceptron on several benchmark and real-world problems.

Original languageEnglish
Title of host publicationGenetic Programming - 18th European Conference, EuroGP 2015, Proceedings
Number of pages14
ISBN (Electronic)9783319165004
Publication statusPublished - 2015
Event18th European Conference on Genetic Programming, EuroGP 2015 - Copenhagen, Denmark
Duration: 8 Apr 201510 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference18th European Conference on Genetic Programming, EuroGP 2015


  • Classification
  • Genetic programming
  • Multidimensional clustering
  • Multiple classes


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