Created by W.Langdon from gp-bibliography.bib Revision:1.7177
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 elsewhere.
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 user.
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.",
Genetic Programming entries for Helio J C Barbosa Heder Soares Bernardino