A new genetic programming framework based on reaction systems

This paper presents a new genetic programming framework called Evolutionary Reaction Systems. It is based on a recently defined computational formalism, inspired by chemical reactions, called Reaction Systems, and it has several properties that distinguish it from other existing genetic programming frameworks, making it interesting and worthy of investigation. For instance, it allows us to express complex constructs in a simple and intuitive way, and it lightens the final user from the task of defining the set of primitive functions used to build up the evolved programs. Given that Evolutionary Reaction Systems is new and it has small similarities with other existing genetic programming frameworks, a first phase of this work is dedicated to a study of some important parameters and their influence on the algorithm's performance. Successively, we use the best parameter setting found to compare Evolutionary Reaction Systems with other well established machine learning methods, including standard tree-based genetic programming. The presented results show that Evolutionary Reaction Systems are competitive with, and in some cases even better than, the other studied methods on a wide set of benchmarks.