Inclusive Genetic Programming

Abstract

The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima. This paper aims to further investigate and prove the efficacy of a GP heuristic proposed in a previous work: the Inclusive Genetic Programming (IGP). Such heuristic can be classified as a niching technique, which performs the evolutionary operations like crossover, mutation and selection by considering the individuals belonging to different niches in order to maintain and exploit a certain degree of diversity in the population, instead of evolving the niches separately to find different local optima. A comparison between a standard formulation of GP and the IGP is carried out on nine different benchmarks coming from synthetic and real world data. The obtained results highlight how the greater diversity in the population, measured in terms of entropy, leads to better results on both training and test data, showing that an improvement on the generalization capabilities is also achieved.

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

“…The particular GP algorithm used in this work is the Inclusive Genetic Programming (IGP), which was originally presented in [29] and later analyzed in [30]. IGP was developed in Python 3 relying on the open source library DEAP [31].…”

“…This is done to reduce the probability of having clones in the population. More on the Inclusive Reproduction and Selection can be found in [30]. Finally, other two peculiarities of the IGP in comparison to other GP formulations are its ability to handle constraints and the fact that is set to evolve more than one GP tree simultaneously.…”