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|Title:||Genetic Programming Techniques in Engineering Applications||Authors:||De Lorenzo, Andrea||Supervisore/Tutore:||Bartoli, Alberto||Cosupervisore:||Medvet, Eric||Issue Date:||1-Apr-2014||Publisher:||Università degli studi di Trieste||Abstract:||
Machine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples.
Machine learning systems, thus, may automatically synthesize programs from data.
This approach is often feasible and cost-effective where manual programming or manual algorithm design is not.
In the last decade techniques based on machine learning have spread in a broad range of application domains.
In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of engineering applications grounded in real world problems.
The problems treated in this work range from the automatic synthesis of regular expressions, to the generation of electricity price forecast, to the synthesis of a model for the tracheal pressure in mechanical ventilation.
The results demonstrate that Genetic Programming is indeed a suitable tool for solving complex problems of practical interest.
Furthermore, several results constitute a significant improvement over the existing state-of-the-art.
The main contribution of this thesis is the design and implementation of a framework for the automatic inference of regular expressions from examples based on Genetic Programming.
First, we will show the ability of such a framework to cope with the generation of regular expressions for solving text-extraction tasks from examples.
We will experimentally assess our proposal comparing our results with previous proposals on a collection of real-world datasets.
The results demonstrate a clear superiority of our approach.
We have implemented the approach in a web application that has gained considerable interest and has reached peaks of more 10000 daily accesses.
Then, we will apply the framework to a popular "regex golf" challenge, a competition for human players that are required to generate the shortest regular expression solving a given set of problems.
Our results rank in the top 10 list of human players worldwide and outperform those generated by the only existing algorithm specialized to this purpose.
Hence, we will perform an extensive experimental evaluation in order to compare our proposal to the state-of-the-art proposal in a very close and long-established research field: the generation of a Deterministic Finite Automata (DFA) from a labelled set of examples.
Our results demonstrate that the existing state-of-the-art in DFA learning is not suitable for text extraction tasks.
We will also show a variant of our framework designed for solving text processing tasks of the search-and-replace form.
A common way to automate search-and-replace is to describe the region to be modified and the desired changes through a regular expression and a replacement expression.
We will propose a solution to automatically produce both those expressions based only on examples provided by user.
We will experimentally assess our proposal on real-word search-and-replace tasks.
The results indicate that our proposal is indeed feasible.
Finally, we will study the applicability of our framework to the generation of schema based on a sample of the eXtensible Markup Language documents.
The eXtensible Markup Language documents are largely used in machine-to-machine interactions and such interactions often require that some constraints are applied to the contents of the documents.
These constraints are usually specified in a separate document which is often unavailable or missing.
In order to generate a missing schema, we will apply and will evaluate experimentally our framework to solve this problem.
In the final part of this thesis we will describe two significant applications from different domains.
We will describe a forecasting system for producing estimates of the next day electricity price.
The system is based on a combination of a predictor based on Genetic Programming and a classifier based on Neural Networks.
Key feature of this system is the ability of handling outliers-i.e., values rarely seen during the learning phase.
We will compare our results with a challenging baseline representative of the state-of-the-art.
We will show that our proposal exhibits smaller prediction error than the baseline.
Finally, we will move to a biomedical problem: estimating tracheal pressure in a patient treated with high-frequency percussive ventilation.
High-frequency percussive ventilation is a new and promising non-conventional mechanical ventilatory strategy.
In order to avoid barotrauma and volutrauma in patience, the pressure of air insufflated must be monitored carefully.
Since measuring the tracheal pressure is difficult, a model for accurately estimating the tracheal pressure is required.
We will propose a synthesis of such model by means of Genetic Programming and we will compare our results with the state-of-the-art.
|Ciclo di dottorato:||XXVI Ciclo||metadata.dc.subject.classification:||INGEGNERIA DELL'INFORMAZIONE||Description:||
|Keywords:||Genetic-Programming; Machine-Learning; Text-Extraction; Prediction; Pattern-Matching||Type:||Doctoral||Language:||en||Settore scientifico-disciplinare:||ING-INF/05 SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI||NBN:||urn:nbn:it:units-12275|
|Appears in Collections:||Ingegneria industriale e dell'informazione|
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