Genetic Programming (GP) is a type of Evolutionary Algorithm (EA), a subset of machine learning. EAs are used to discover solutions to problems humans do not know how to solve, directly. …

Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively …

Early work that set the stage for current genetic programming research topics and applications is diverse, and includes software synthesis and repair, predictive modelling, data mining [19], …

GeneticEngine is a grammar-guided genetic programming (GGGP) framework, in which the grammars are defined using Python classes. GeneticEngine supports single- and multi …

Tree-based Genetic Programming. In tree-based GP, the computer programs are represented in tree structures that are evaluated recursively to produce the resulting multivariate expressions. …

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular …

Genetic Programming Theory & Practice is a small, invitation-only workshop hosted annually by the Center for the Study of Complex Systems at the University of Michigan, in Ann Arbor, MI. …

According to “A Field Guide to Genetic Programming”, there are three basic steps to generational, Tree-based GP: Generate an initial, stochastic population. Iteratively perform selection, …

Depending on the specific language and genetic operators used, stack-based genetic programming can have a variety of advantages over tree-based genetic programming.

We present results from illustrative experiments using our reference implementation of the problems in the PushGP genetic programming system. The results show that the problems in …