[论文解读] Gene Expression Programming: a New Adaptive Algorithm for Solving Problems

Cândida Ferreira|ArXiv.org|Feb 25, 2001

Evolutionary Algorithms and Applications参考文献 20被引用 2,016

一句话总结

Introduces gene expression programming (GEP), a genotype/phenotype genetic algorithm that evolves linear genomes which encode expression trees, enabling efficient program creation and solving diverse problems.

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.

研究动机与目标

• Introduce gene expression programming as a new technique for automatic computer program creation.
• Demonstrate how separating genome and expression tree enhances search efficiency compared to existing adaptive methods.
• Show versatility of GEP across problems such as symbolic regression, sequence induction, block stacking, cellular automata, and boolean learning.

提出的方法

• Represent chromosomes as linear genes organized with a head and tail.
• Encode expression trees from chromosomes which are subjected to mutation, transposition, and recombination operators.
• Use a separate genome and expression tree to enable efficient evolution and robust selection of programs.
• Apply genetic operators including one- and two-point recombination to modify chromosomes.

实验结果

研究问题

• RQ1 How can a genotype/phenotype separation improve the efficiency of evolving computer programs?
• RQ2 Can GEP effectively solve a range of problem types, including symbolic regression, sequence induction, and boolean learning?
• RQ3 What genetic operators best facilitate the evolution of accurate expression trees from linear chromosomes?

主要发现

• GEP encodes expression trees from linear chromosomes with a head and tail.
• The approach uses mutations and various transpositions and recombinations to explore the search space.
• The genome/tree separation yields high efficiency that surpasses existing adaptive techniques.
• GEP is demonstrated on problems including symbolic regression, sequence induction, block stacking, cellular automata rules for density-classification, and 11-multiplexer/GP rule problems.