Portuguese Conference on Artificial Intelligence

EPIA 2015: Progress in Artificial Intelligence pp 280-285 | Cite as

Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9273)

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Ivo Gonçalves
    • 1
    • 2
  • Sara Silva
    • 1
    • 2
    • 3
  • Carlos M. Fonseca
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.BioISI - Biosystems & Integrative Sciences Institute, Faculty of SciencesUniversity of LisbonCampo Grande, LisbonPortugal
  3. 3.NOVA IMS, Universidade Nova de LisboaLisbonPortugal

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