Unsure when to Stop?: Ask Your Semantic Neighbors

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ivo Goncalves and Sara Silva and Carlos M. Fonseca and 
                 Mauro Castelli",
  title =        "Unsure when to Stop?: Ask Your Semantic Neighbors",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "929--936",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071328",
  DOI =          "doi:10.1145/3071178.3071328",
  acmid =        "3071328",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming,
                 generalization, geometric semantic genetic programming,
                 overfitting, semantic learning machine, stopping
  month =        "15-19 " # jul,
  abstract =     "In iterative supervised learning algorithms it is
                 common to reach a point in the search where no further
                 induction seems to be possible with the available data.
                 If the search is continued beyond this point, the risk
                 of overfitting increases significantly. Following the
                 recent developments in inductive semantic stochastic
                 methods, this paper studies the feasibility of using
                 information gathered from the semantic neighbourhood to
                 decide when to stop the search. Two semantic stopping
                 criteria are proposed and experimentally assessed in
                 Geometric Semantic Genetic Programming (GSGP) and in
                 the Semantic Learning Machine (SLM) algorithm (the
                 equivalent algorithm for neural networks). The
                 experiments are performed on real-world
                 high-dimensional regression datasets. The results show
                 that the proposed semantic stopping criteria are able
                 to detect stopping points that result in a competitive
                 generalization for both GSGP and SLM. This approach
                 also yields computationally efficient algorithms as it
                 allows the evolution of neural networks in less than 3
                 seconds on average, and of GP trees in at most 10
                 seconds. The usage of the proposed semantic stopping
                 criteria in conjunction with the computation of optimal
                 mutation/learning steps also results in small trees and
                 neural networks.",
  notes =        "Also known as
                 \cite{Goncalves:2017:USA:3071178.3071328} GECCO-2017 A
                 Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Ivo Goncalves Sara Silva Carlos M Fonseca Mauro Castelli