Mt-CGP

Abstract

The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.

Prediction of the Minimum Spouting Velocity by Genetic Programming Approach

“…The most widely used genetic programming technique has been reported by Koza . This technique has been used in several applications, such as logical design, data classification, digital circuit area optimization, neural network evolution, , and image processing. − …”