Immune System Programming: A Machine Learning Approach Based on Artificial Immune Systems Enhanced by Local Search

Abstract

The foundation of machine learning is to enable computers to automatically solve certain problems. One of the main tools for achieving this goal is genetic programming (GP), which was developed from the genetic algorithm to expand its scope in machine learning. Although many studies have been conducted on GP, there are many questions about the disruption effect of the main GP breeding operators, i.e., crossover and mutation. Moreover, this method often suffers from high computational costs when implemented in some complex applications. This paper presents the meta-heuristics programming framework to create new practical machine learning tools alternative to the GP method. Furthermore, the immune system programming with local search (ISPLS) algorithm is composed from the proposed framework to enhance the classical artificial immune system algorithm with the tree data structure to deal with machine learning applications. The ISPLS method uses a set of breeding procedures over a tree space with gradual changes in order to surmount the defects of GP, especially the high disruptions of its basic operations. The efficiency of the proposed ISPLS method was proven through several numerical experiments, including promising results for symbolic regression, 6-bit multiplexer and 3-bit even-parity problems.