The genetic programming (GP) paradigm is a new approach to inductively forming programs that describe a particular problem. The use of natural selection based on a fitness ]unction...
In this paper we explain how we applied genetic programming to behavior-based team coordination in the RoboCup Soccer Server domain. Genetic programming is a promising new method f...
Sean Luke, Charles Hohn, Jonathan Farris, Gary Jac...
Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest t...
: This paper demonstrates that a design for a low-distortion high-gain 96 decibel (64,860 -to-1) operational amplifier (including both circuit topology and component sizing) can be...
John R. Koza, David Andre, Forrest H. Bennett III,...
In spirit of the earlier works done by Arthur (1992) and Palmer et al. (1993), this paper models speculators with genetic programming (GP) in a production economy (Muthian Economy)...
Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting ...
The question that we investigate in this paper is, whether it is possible for Genetic Programming to extract certain regularities from raw time series data of human speech. We exam...
We use the genetic programming (GP) paradigm for two tasks. The first task given a GP is the generation of rules for the target / clutter classification of a set of synthetic apert...
In genetic programming a general consensus is that the population should be as large as practically possible or sensible. In this paper we examine a batch of problems of combinato...
This paper examines some of the reporting and research practices concerning empirical work in genetic programming. We describe several common loopholes and offer three case studie...
Jason M. Daida, Derrick S. Ampy, Michael Ratanasav...