Solving even-parity problems using traceless genetic programming

TL;DR

This paper introduces traceless genetic programming (TGP), a novel hybrid approach that evolves digital circuits for the even-parity problem without explicitly storing programs, significantly outperforming standard GP.

Contribution

The paper presents TGP, a new GP variant that combines building and representing individuals without storing programs, advancing genetic programming techniques.

Findings

TGP outperforms standard GP by several orders of magnitude.

TGP effectively evolves digital circuits for the even-parity problem.

TGP uses crossover and insertion operators in a hybrid framework.

Abstract

A genetic programming (GP) variant called traceless genetic programming (TGP) is proposed in this paper. TGP is a hybrid method combining a technique for building individuals and a technique for representing individuals. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with TGP: crossover and insertion. TGP is applied for evolving digital circuits for the even-parity problem. Numerical experiments show that TGP outperforms standard GP with several orders of magnitude.