Inference of compact nonlinear dynamic models by epigenetic local search

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "William {La Cava} and Kourosh Danai and Lee Spector",
  title =        "Inference of compact nonlinear dynamic models by
                 epigenetic local search",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2016",
  volume =       "55",
  pages =        "292--306",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, System
                 identification, Dynamical systems, Differential
                 equations, Symbolic regression",
  ISSN =         "0952-1976",
  URL =          "",
  DOI =          "doi:10.1016/j.engappai.2016.07.004",
  abstract =     "We introduce a method to enhance the inference of
                 meaningful dynamic models from observational data by
                 genetic programming (GP). This method incorporates an
                 inheritable epigenetic layer that specifies active and
                 inactive genes for a more effective local search of the
                 model structure space. We define several GP
                 implementations using different features of
                 epigenetics, such as passive structure, phenotypic
                 plasticity, and inheritable gene regulation. To test
                 these implementations, we use hundreds of data sets
                 generated from nonlinear ordinary differential
                 equations (ODEs) in several fields of engineering and
                 from randomly constructed nonlinear ODE models. The
                 results indicate that epigenetic hill climbing
                 consistently produces more compact dynamic equations
                 with better fitness values, and that it identifies the
                 exact solution of the system more often, validating the
                 categorical improvement of GP by epigenetic local
                 search. The results further indicate that when faced
                 with complex dynamics, epigenetic hill climbing reduces
                 the computational effort required to infer the correct
                 underlying dynamics. We then apply the method to the
                 identification of three real-world systems: a cascaded
                 tanks system, a chemical distillation tower, and an
                 industrial wind turbine. We analyse its solutions in
                 comparison to theoretical and black-box approaches in
                 terms of accuracy and intelligibility. Finally, we
                 analyze population homology to evaluate the efficiency
                 of the method. The results indicate that the epigenetic
                 implementations provide protection from premature
                 convergence by maintaining diversity in silenced
                 portions of programs.",

Genetic Programming entries for William La Cava Kourosh Danai Lee Spector