Special Session on
Nature-Inspired Techniques for Symbolic Regression

2013 IEEE Congress on Evolutionary Computation (CEC 2013)

June 20-23, Fiesta Americana Grand Coral Beach Hotel, Cancun, Mexico

Douglas A. Augusto 1
Heder S. Bernardino 2
Helio J.C. Barbosa 1, 2
1 National Laboratory for Scientific Computing (LNCC), BRAZIL
2 Department of Computer Science at Federal University of Juiz de Fora (DCC/UFJF), BRAZIL

Important Dates
Paper submission
March 15, 2013
Please check the CEC 2013 web site for additional information.

Program Committee (already confirmed)
  • Vladan Babovic
    National Univ. of Singapore
  • Steven Gustafson
    GE Global Research Center
  • Bob McKay
    Seoul National University
  • Sara Silva
    INESC-ID Lisboa
  • Leonardo Vanneschi
    NOVA University of Lisbon
  • Ekaterina Vladislavleva
    University of Antwerp
For centuries researchers have been interested in automating the knowledge discovery process. When the objective is to get insights regarding data sets, models can be considered among the desirable artifacts produced by this task.
Traditionally, the form of the model is explicitly defined by a domain specialist, leaving just coefficients or parameters to be further adjusted. However, it is possible to go beyond; in symbolic regression the structure of the model is no longer predefined by the user but rather included as part of the problem.
Symbolic regression aims at finding symbolic descriptions, usually as mathematical expressions, decision rules, or even programs in a certain language, in order to describe and communicate new knowledge as well as assist the decision making process in various domains.
The bio-inspired genetic programming paradigm, originally designed to find computer programs in arbitrary human-readable languages, is well-suited and widely applied to symbolic regression problems. However, other nature-inspired search engines can potentially be adopted in symbolic regression.
The session seeks to promote the presentation and discussion of innovative techniques for symbolic regression problems, model inference, and knowledge discovery involving (but not limited to):
  • Evolutionary algorithms
  • Swarm intelligence
  • Immune Systems
  • Physically-inspired techniques
  • Novel nature-inspired techniques
  • New representations and/or operators
  • Any genetic programming (GP) variant
  • Parallel and distributed algorithms
  • Empirical studies of GP performance and behavior
as well as new applications, specially in real-world problems.