Semantic learning machine: A feedforward neural network construction algorithm inspired by geometric semantic genetic programming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Geometric Semantic Genetic Programming (GSGP) is a recently proposed form of Genetic Programming in which the fitness landscape seen by its variation operators is unimodal with a linear slope by construction and, consequently, easy to search. This is valid across all supervised learning problems. In this paper we propose a feedforward Neural Network construction algorithm derived from GSGP. This algorithm shares the same fitness landscape as GSGP, which allows an efficient search to be performed on the space of feedforward Neural Networks, without the need to use backpropagation. Experiments are conducted on real-life multidimensional symbolic regression datasets and results show that the proposed algorithm is able to surpass GSGP, with statistical significance, in terms of learning the training data. In terms of generalization, results are similar to GSGP.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence
Subtitle of host publication17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PublisherSpringer-Verlag
Pages280-285
Number of pages6
Volume9273
ISBN (Print)9783319234847
DOIs
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015
CountryPortugal
CityCoimbra
Period8/09/1511/09/15

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  • Cite this

    Gonçalves, I., Silva, S. G. O. D., & Fonseca, C. M. (2015). Semantic learning machine: A feedforward neural network construction algorithm inspired by geometric semantic genetic programming. In Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings (Vol. 9273, pp. 280-285). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9273). Springer-Verlag. https://doi.org/10.1007/978-3-319-23485-4_28