Gene Expression Programming: a New Adaptive Algorithm for Solving Problems

TL;DR

Gene expression programming is a novel genetic algorithm that encodes expression trees in linear chromosomes, enabling efficient problem solving across diverse domains like symbolic regression and boolean learning.

Contribution

It introduces a new genotype/phenotype genetic algorithm with a unique chromosome structure and genetic operators, outperforming existing adaptive techniques.

Findings

High efficiency in solving diverse problems

Versatility across symbolic regression and boolean learning

Superior performance compared to existing methods

Abstract

Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and one- and two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.