4 research outputs found
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach
The definition of a concise and effective testbed for Genetic Programming
(GP) is a recurrent matter in the research community. This paper takes a new
step in this direction, proposing a different approach to measure the quality
of the symbolic regression benchmarks quantitatively. The proposed approach is
based on meta-learning and uses a set of dataset meta-features---such as the
number of examples or output skewness---to describe the datasets. Our idea is
to correlate these meta-features with the errors obtained by a GP method. These
meta-features define a space of benchmarks that should, ideally, have datasets
(points) covering different regions of the space. An initial analysis of 63
datasets showed that current benchmarks are concentrated in a small region of
this benchmark space. We also found out that number of instances and output
skewness are the most relevant meta-features to GP output error. Both
conclusions can help define which datasets should compose an effective testbed
for symbolic regression methods.Comment: 8 pages, 3 Figures, Proceedings of Genetic and Evolutionary
Computation Conference Companion, Kyoto, Japa
Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming
Genetic Programming (GP) is an heuristic method that can be applied to many
Machine Learning, Optimization and Engineering problems. In particular, it has
been widely used in Software Engineering for Test-case generation, Program
Synthesis and Improvement of Software (GI).
Grammar-Guided Genetic Programming (GGGP) approaches allow the user to refine
the domain of valid program solutions. Backus Normal Form is the most popular
interface for describing Context-Free Grammars (CFG) for GGGP. BNF and its
derivatives have the disadvantage of interleaving the grammar language and the
target language of the program.
We propose to embed the grammar as an internal Domain-Specific Language in
the host language of the framework. This approach has the same expressive power
as BNF and EBNF while using the host language type-system to take advantage of
all the existing tooling: linters, formatters, type-checkers, autocomplete, and
legacy code support. These tools have a practical utility in designing software
in general, and GP systems in particular.
We also present Meta-Handlers, user-defined overrides of the tree-generation
system. This technique extends our object-oriented encoding with more
practicability and expressive power than existing CFG approaches, achieving the
same expressive power of Attribute Grammars, but without the grammar vs target
language duality.
Furthermore, we evidence that this approach is feasible, showing an example
Python implementation as proof. We also compare our approach against textual
BNF-representations w.r.t. expressive power and ergonomics. These advantages do
not come at the cost of performance, as shown by our empirical evaluation on 5
benchmarks of our example implementation against PonyGE2. We conclude that our
approach has better ergonomics with the same expressive power and performance
of textual BNF-based grammar encodings
Ensemble SGE
Este documento diz respeito a um projeto de investigação que decorreu no âmbito de
projeto de final de curso do Mestrado em Informática e Sistemas, ramo de Tecnologias
de Informação e Conhecimento que decorreu no Instituto Superior de Engenharia de
Coimbra. Está integrado na área da aprendizagem automática e tem como principal
objetivo desenvolver uma nova framework suportada pelo SGE para resolver
problemas de aprendizagem supervisionada, e tem o nome de Ensemble SGE.
O Ensemble SGE, utiliza o SGE que é um algoritmo de evolução automática de
programas, para gerar vários modelos capazes de resolver um problema. E
posteriormente utiliza técnicas de aprendizagem por Ensemble para agregar alguns
dos modelos gerados e produzir um Ensemble.
Neste trabalho foram abordados 3 problemas de regressão simbólica. Duas
aproximações a funções conhecidas, polinómio de quarto grau e o polinómio de Pagie
e por fim Boston Housing, um problema em que dadas características de uma casa é
necessário prever o seu preço.
Os resultados deste projeto são positivos, é demonstrado que é possível obter
Ensembles capazes de resolver alguns problemas de uma melhor forma, que o melhor
modelo gerado pelo SGE. A performance obtida pela utilização de Ensembles é maior
comparativamente a modelos simples gerados pelo SGE. A framework foi
implementada e disponibilizada com possíveis casos de teste.
Concluindo, a escolha dos modelos constituintes do Ensemble é a decisão mais
importante, pois não foi encontrada nenhuma maneira exata de o fazer, ou seja,
apenas por métodos experimentais. O Ensemble SGE também consegue detetar
situações de overfitting mais cedo que o melhor modelo do SGE ao longo das
gerações. Isto porque o Ensemble SGE utiliza vários indivíduos de uma população
Neuroevolution trajectory networks : illuminating the evolution of artificial neural networks
Neuroevolution is the discipline whereby ANNs are automatically generated using EC. This field began with the evolution of dense (shallow) neural networks for reinforcement learning task; neurocontrollers capable of evolving specific behaviours as required.
Since then, neuroevolution has been used to discover architectures and hyperparameters of Deep Neural Networks, in ways never before conceived by human experts, with many achieving state-of-the-art results. Similar to other types of EAs, there is a wide variety of neuroevolution algorithms constantly being introduced. However, there is a lack of effective tools to examine these systems and assess whether they share underlying principles.
This thesis proposes Neuroevolution Trajectory Networks (NTNs), an advanced visualisation tool that leverages complex networks to explore the intrinsic mechanisms inherent in the evolution of neural networks. In this research the tool was developed as a specialised version of Search Trajectory Networks, and it was particularly instantiated to illuminate the behaviour of algorithms navigating neuroevolution search spaces.
Throughout the progress, this technique has been progressively applied from systems of shallow network evolution, to deep neural networks. The examination has focused on explicit characteristics of neuroevolution system. Specifically, the learnings achieved highlighted the importance of understanding the role of recombination in neuroevolution, revealing critical inefficiencies that hinder overall algorithm performance. A relation between neurocontrollers' diversity and exploration exists, as topological structures can influence the behavioural characterisations and the diversity generation of different search strategies. Furthermore, our analytical tool has offered insights into the favoured dynamics of transfer learning paradigm in the deep neuroevolution of Convolutional Neural Networks; shedding light on promising avenues for further research and development.
All of the above have offered substantial evidence that this advanced tool can be regarded as a specialised observational technique to better understand the inner mechanics of neuroevolution and its specific components, beyond the assessment of accuracy and performance alone. This is done so that collective efforts can be concentrated on aspects that can further enhance the evolution of neural networks.
Illuminating their search spaces can be seen as a first step to analysing neural network compositions