We present the application of Genetic Programming (GP) in Branch and Bound (B&B) based Mixed Integer Linear Programming (MIP). The hybrid architecture introduced employs GP as a node selection expression generator: a GP run, embedded into the B&B process, exploits the characteristics of the particular MIP problem being solved, evolving a problem-specific node selection method. The evolved metho...

The transportation network design problem (NDP) with multiple objectives and demand uncertainty was originally formulated as a spectrum of stochastic multi-objective programming models in a bi-level programming framework. Solving these stochastic multi-objective NDP (SMONDP) models directly requires generating a family of optimal solutions known as the Pareto-optimal set. For practical implemen...

Genetic Programming (GP) is a method to evolve computer programs. And the reason we would want to try this is because, as anyone who’s done even half a programming course would know, computer programming is hard. Automatic programming has been the goal of computer scientists for a number of decades. Scientists would like to be able to give the computer a problem and ask the computer to build a ...

Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and co...

Intelligent robot navigation can be achieved using a control system comprised of a collection of special-purpose motion routines, or behaviors. An approach to behavior coordination in multi-behavior systems is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavi...

The novel evolutionary artificial intelligence formalism namely, genetic programming (GP) a branch of genetic algorithms is utilized to develop mathematical models based on input-output data, instead of conventional regression and neural network modeling techniques which are commonly used for this purpose. This paper summarizes the available MATLAB toolboxes and their features. Glucose to gluco...

Genetic Programming (GP) is a machine learning technique that writes computer programs, automatically. Although individual researchers used GP techniques in the 1960’s and 1970’s, GP emerged as a distinct discipline in 1992. Since that time, over one thousand academic studies have been published in the field and, in 1998, commercial GP software – Discipulus – reached the market. Discipulus is a...