Genetic Programming in Civil, Structural and Environmental Engineering

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

  author =       "Helio J. C. Barbosa and Heder S. Bernardino",
  title =        "Genetic Programming in Civil, Structural and
                 Environmental Engineering",
  journal =      "Computational Technology Reviews",
  year =         "2011",
  volume =       "4",
  pages =        "115--145",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Civil-Comp",
  ISSN =         "2044-8430",
  URL =          "",
  DOI =          "doi:10.4203/ctr.4.5",
  abstract =     "Soft computing techniques have been receiving
                 considerable attention in recent years due to their
                 wide applicability and low ratio of implementation
                 effort to succeed in producing good results. In civil
                 and environmental engineering one is often faced with
                 the problem of inferring a mathematical model from a
                 set of observed data. Also, in structural engineering,
                 nature-inspired techniques, especially evolutionary
                 algorithms, have been extensively applied, mainly to
                 parametric design optimization problems.

                 This paper provides an overview of the applications of
                 one of the most versatile soft computing tools
                 available - genetic programming - to relevant design,
                 optimization, and identification of problems arising in
                 civil, structural, and environmental engineering.
                 Genetic programming (GP) is a domain-independent
                 sub-area of the evolutionary computation field. The
                 candidate solutions are referred to as programs, a
                 high-level structure able to represent a large class of
                 computational artefacts. A program can be a standard
                 computer program, a numerical function or a classifier
                 in symbolic form, a candidate design (such as the
                 structure of a building), among many other
                 possibilities. In the following sections tree-based,
                 linear, and graph-based GPs are discussed. Moreover,
                 grammatical evolution (GE) is presented in some detail,
                 a relatively recent GP technique in which candidate
                 solution's genotypes are binary encoded and space
                 transformations create the programs employing a
                 user-defined grammar.

                 The most common classes of problems in civil,
                 structural, and environmental engineering in which GP
                 has been applied are loosely grouped here into two
                 large classes, namely model inference and design. Both
                 types of problems correspond to activities
                 traditionally assigned only to humans, as they require
                 intelligence and creativity not (yet) available

                 Some representative papers from the literature were
                 reviewed and are summarized in nine tables. The tables
                 indicate the reference number, the GP technique
                 adopted, the class of problem considered, a short
                 description of the application, and the main results
                 and conclusions of the paper. Our survey indicated a
                 much larger number of papers dealing with model
                 inference than with design applications in the civil,
                 structural, and environmental engineering literature.
                 Also, as expected, the standard tree-based genetic
                 programming (TGP) is by far the most often adopted
                 technique. Contrary to our expectations, gene
                 expression programming (GEP) seems to be more popular
                 than GE, which is probably due to the fact that GE,
                 although more elegant and flexible, requires the
                 specification of a problem dependent grammar by the

                 Genetic programming has been proving its versatility in
                 many different fields. Due to its great expressiveness,
                 GP is able to evolve complex artifacts, either when
                 inducing understandable and communicable models or
                 generating novel designs.",
  notes =        "Laboratorio Nacional de Computacao Cientifica,
                 Petropolis, RJ, Brazil",

Genetic Programming entries for Helio J C Barbosa Heder Soares Bernardino