Created by W.Langdon from gp-bibliography.bib Revision:1.7416
Conclusion. This paper establishes a relationship between the CV and GP as its numerical methods. The central goal of CV is determining the functional that attend to some constraints solving fundamentals differential equations by analytical methods while GP try to obtain the solution applying genetic operators in tree coded chromosomes. The differential displacement in analytical solution assumes the format of a change into the functional structure through the application of crossover and mutation operators. The action integral has its similar in the fitness function, with in the same way is obtained during all solution interval time. The initial condition appears in both approaches defining a realisation of an intrinsic solution (we termed Cognitive Structure) that is holistic, i.e., complete and self-contained. It?s a solution not for one single problem, but for a large class of similar problems. Under this point of view, we would divide any problem solution as two different levels: one for the CS search, and the other to its adaptation to one realization. A general overview is: the information available of the problem feeds GP software, with after some generations obtain the cognitive structure of the problem, or the best available at this moment with minimise the fitness function, i.e., the action for the system. This structure needs to be adapted to the real conditions of the system.",
Genetic Programming entries for James Cunha Werner