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Ensembled Geometric Semantic Genetic Programming: An ensemble-based initialization technique for Geometric Semantic Genetic Programming
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Resumo(s)
Machine learning is a scientific field that seeks to empower computers with the ability
to learn without having to be explicitly programmed. Given the ever growing number
of sophisticated intelligent machine learning algorithms, investigators can find themselves
“stuck” in a time and resources consuming process of model searching, tuning
and testing. Bearing this in mind, Ensemble Methods and meta-learning algorithms
have emerged as an attempt to automate the process of combining several different
models in an intelligent, adaptive way.
Within the field of machine learning, a specific set of algorithms, called Evolutionary
Algorithms, mimic Darwin’s Theory of Evolution. As their name indicates, these
algorithms incorporate concepts of evolution to the task learning process. They do
so by evolving a set of individuals (i.e., possible solutions to a given problem) under
pressures of natural selection and "survival of the fittest" mechanisms.
Genetic Programming (GP) is an Evolutionary Algorithm that evolves computer
programs (i.e., individuals) in order to perform a mapping between input and output
features. Its extension, Geometric Semantic Genetic Programming (GSGP), allows us
to perform the evolution and variation of the individuals on the semantic space (i.e.,
the space in which the output vectors lay) rather than on their syntax based structure.
The generation of the initial population, commonly known as the initialization, has
been proven to be a key factor for both GP and GSGP’s performance.
This work proposes an initialization technique for GSGP that utilizes the combinatory
power of Ensemble Methods in order to generate a “fit” initial population
based on the predictions of a wide variety of machine learning algorithms, called Base
Learners, making use of GSGP’s ability to evolve individuals solely based on their
semantics (i.e., predictions). This initialization technique is called Ensembled Geometric
Semantic Genetic Programming as it utilizes GSGP as a learning combiner that
merges the knowledge obtained from different machine learning techniques by evolving
their semantics via crossing between and mutating them over the course of several
generations.
The performance of the proposed initialization method (EGSGP) was tested on three different case studies. Results show that EGSGP significantly outperforms traditional
GSGP (with the exception of one case study where EGSGP’s improved performance
in comparison to GSGP did not translate into statistically significant results).
Additionally, results show that EGSGP produces better results than the best
base learner from the initial population being, thus, able to improve it. This improvement
was present in all case studies only yielding, however, statistically significant
results in one of them.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Genetic Programming Geometric Semantic Genetic Programming Initialization methods Ensemble methods Meta-learning Machine Learning Evolutionary Algorithms