Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

Title: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems

Research Question: Can Gene Expression Programming (GEP) be a more efficient and versatile algorithm for solving problems compared to existing techniques like Genetic Algorithms (GAs) and Genetic Programming (GP)?

Methodology: The study introduces GEP as a new technique for creating computer programs. It 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 through mutation, transposition, root transposition, gene transposition, gene recombination, 1-point and 2-point recombination. The chromosomes encode expression trees, which are the objects of selection.

Results: GEP surpasses GP in more than four orders of magnitude in the density-classification problem, demonstrating its efficiency and versatility. It also outperforms GP in other problems such as symbolic regression, sequence induction, block stacking, 11-multiplexer, and GP rule problems.

Implications: The advantages of GEP are clear from nature. It has simple, linear, compact, and easy-to-manipulate chromosomes. The interplay of chromosomes and expression trees creates a universal translation system, allowing the translation of chromosome language into expression tree language. This makes GEP a life-like, complex system capable of adaptation and evolution. Its versatility and efficiency make it a promising technique for solving a wide range of problems.

Link to Article: https://arxiv.org/abs/0102027v1 Authors: arXiv ID: 0102027v1