Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors

International audienceElectrical power networks are heavily monitored systems, requiring operators to perform intricate information synthesis before understanding the underlying network state. Our study aims at helping this synthesis step by automatically creating features from the sensor data. We propose a supervised feature extraction approach using a grammar-guided evolution, which outputs interpretable and dimensionally consistent features. Operations restrictions on dimensions are introduced in the learning process through context-free grammars. They ensure coherence with physical laws, dimensional-consistency, and also introduce technical expertise in the created features. We compare our approach to other state-of-the-art feature extraction methods on a real dataset taken from the French electrical network sensors

Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii

Aplicação de Algoritmos Evolucionários à Extracção de Padrões Musicais

Dissertação de Mestrado em Engenharia Informática apresentada á Faculdade de Ciências e Tecnologia da Universidade de Coimbra.A extracção de padrões é um problema que se coloca em várias áreas como, por exemplo, a biologia molecular ou a área financeira, e que pode ser considerado, do ponto de vista da inteligência artificial, como uma forma de aprendizagem não supervisionada. No domínio musical, o problema pode ser definido, informalmente, da seguinte forma: dada uma peça musical (ou várias), identificar as partes dessa peça que se repitam, aproximadamente ou não, e que possuam um significado relevante no contexto dessa peça. O objectivo deste trabalho consistiu em estudar a viabilidade da aplicação de algoritmos evolucionários ao problema da extracção de padrões musicais. Para levar a cabo o estudo proposto desenvolvemos duas abordagens utilizando dois tipos diferentes de algoritmos evolucionários: a programação genética e os algoritmos genéticos. Em cada uma das abordagens o objectivo é essencialmente o mesmo: encontrar uma segmentação de uma peça que permita identificar os padrões mais importantes nela existentes. Devido às características de cada um dos algoritmos, a representação utilizada para os indivíduos é diferente. Assim, enquanto que na abordagem baseada em programação genética cada indivíduo é um programa que produz como resultado uma determinada peça, constituindo ao mesmo tempo uma descrição da sua estrutura de segmentos, na abordagem baseada em algoritmos genéticos cada indivíduo consiste numa sequência de símbolos que representa uma hipótese de segmentação da peça a analisar. Embora as funções de avaliação utilizadas nas duas abordagens também sejam diferentes, ambas beneficiam os indivíduos que apresentem o conjunto dos padrões mais importantes existentes na peça. Para ambas as abordagens foi também desenvolvido um método que permite realizar uma segunda segmentação de uma peça a partir dos segmentos identificados na primeira segmentação. Os resultados experimentais obtidos com a abordagem baseada em programação genética que desenvolvemos permitem-nos verificar que esta abordagem apresenta bastantes dificuldades na resolução deste tipo de problemas. Pelo contrário, a abordagem baseada em algoritmos genéticos permitiu obter resultados que nos levam a considerar que a aplicação desta abordagem a este tipo de problemas é viável.Pattern extraction is a problem that occurs in several areas like, for example, molecular biology and finance, and can be viewed, from the point of view of artificial intelligence, as a kind of unsupervised learning. In the musical domain, the problem can be informally defined in the following way: given a musical piece (or more), identify the meaningful recurrent parts of that piece. The goal of this work is to study the viability of applying evolutionary algorithms to the problem of musical pattern extraction. In order to take this study, we develop two approaches based on two different types of evolutionary algorithms: genetic programming and genetic algorithms. The goal in both approaches is essentially the same: find a segmentation of a musical piece that allows the identification of the most meaningful patterns that exist in that piece. Due to the character of each type of algorithm, the representation used to represent individuals in each approach its different. Hence, while in the genetic programming based approach an individual is a program that produces as a result a musical piece, being at the same time a description of the structure of that piece, in the genetic algorithms based approach each individual is a sequence of symbols that represent a possible segmentation of the musical piece that is being analyzed. The two approaches also use different fitness functions, but both have in common the fact that they give a better fitness value to individuals that present the set of most meaningful patterns. For both approaches we also developed a method to make a second segmentation of a musical piece using the segments identified in the first segmentation. The experimental results obtained with the genetic programming based approach allowed us to verify that this approach has great difficulties in the resolution of this type of problems. On the contrary, with the genetic algorithms based approach we obtained results that allow us to believe that this approach can be useful in the resolution of this type of problems

Three-cornered coevolution learning classifier systems for classification

This thesis introduces a Three-Cornered Coevolution System that is capable of addressing classification tasks through coevolution (coadaptive evolution) where three different agents (i.e. a generation agent and two classification agents) learn and adapt to the changes of the problems without human involvement. In existing pattern classification systems, humans usually play a major role in creating and controlling the problem domain. In particular, humans set up and tune the problem’s difficulty. A motivation of the work for this thesis is to design and develop an automatic pattern generation and classification system that can generate various sets of exemplars to be learned from and perform the classification tasks autonomously. The system should be able to automatically adjust the problem’s difficulty based on the learners’ ability to learn (e.g. determining features in the problem that affect the learners’ performance in order to generate various problems for classification at different levels of difficulty). Further, the system should be capable of addressing the classification tasks through coevolution (coadaptive evolution), where the participating agents learn and adapt to the changes of the problems without human participation. Ultimately, Learning Classifier System (LCS) is chosen to be implemented in the participating agents. LCS has several potential characteristics, such as interpretability, generalisation capability and variations in representation, that are suitable for the system. The work can be broken down into three main phases. Phase 1 is to develop an automated evolvable problem generator to autonomously generate various problems for classification, Phase 2 is to develop the Two-Cornered Coevolution System for classification, and Phase 3 is to develop the Three-Cornered Coevolution System for classification. Phase 1 is necessary in order to create a set of problem domains for classification (i.e. image-based data or artificial data) that can be generated automatically, where the difficulty levels of the problem can be adjusted and tuned. Phase 2 is needed to investigate the generation agent’s ability to autonomously tune and adjust the problem’s difficulty based on the classification agent’s performance. Phase 2 is a standard coevolution system, where two different agents evolve to adapt to the changes of the problem. The classification agent evolves to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the learner’s ability to learn. Phase 3 is the final research goal. This phase develops a new coevolution system where three different agents evolve to adapt to the changes of the problem. Both of the classification agents evolve to learn various classification problems, while the generation agent evolves to tune and adjust the problem’s difficulty based on the classification agents’ ability to learn. The classification agents use different styles of learning techniques (i.e. supervised or reinforcement learning techniques) to learn the problems. Based on the classification agents’ ability (i.e. the difference in performance between the classification agents) the generation agent adjusts and creates various problems for classification at different levels of difficulty (i.e. various ‘hard’ problems). The Three-Cornered Coevolution System offers a great potential for autonomous learning and provides useful insight into coevolution learning over the standard studies of pattern recognition. The system is capable of autonomously generating various problems, learning and providing insight into each learning system’s ability by determining the problem domains where they perform relatively well. This is in contrast to humans having to determine the problem domains