ABSTRACT
This chapter provides a critical review of Cartesian Genetic Programming algorithms that make use of hybrid approaches. The generation of computer programs utilizing Genetic Programming techniques has drawn the interest of many scientists after the publication of J. Koza’s book. Metaheuristics are self-learning methods originated from the field of intelligent systems that arise in biology, physics and other knowledge domains. Single-solution methods are metaheuristics that maintain only one candidate solution at a time. Population-based methods keep a group of solutions instead of just one. Most of those algorithms are nature-inspired, a well-known group of techniques termed as Evolutionary Algorithms. Evolution Strategies are inspired by the species-level process of evolution. Biogeography-Based Optimization is an evolutive algorithm that has the science of biogeography as inspiration. Harmony Search is a population-based method that mimics the behavior of an orchestra while trying to compose a harmonic melody.
