Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation., Paris : France (2008

Probabilistic Maximum Set Cover with Path Constraints for Informative Path Planning

We pose a new formulation for informative path planning problems as a generalisation of the well-known maximum set cover problem. This new formulation adds path constraints and travel costs, as well as a probabilistic observation model, to the maximum set cover problem. Our motivation is informative path planning applications where the observation model can be naturally encoded as overlapping subsets of a set of discrete elements. These elements may include features, landmarks, regions, targets or more abstract quantities, that the robot aims to observe while moving through the environment with a given travel budget. This formulation allows directly modelling the dependencies of observations from different viewpoints. We show this problem is NP-hard and propose a branch and bound tree search algorithm. Simulated experiments empirically evaluate the bounding heuristics, several tree expansion policies and convergence rate towards optimal. The tree pruning allows finding optimal or bounded-approximate solutions in a reasonable amount of time, and therefore indicates our work is suitable for practical applications

Advances in parameterisation, optimisation and pruning of neural networks

Les réseaux de neurones sont une famille de modèles de l'apprentissage automatique qui sont capable d'apprendre des tâches complexes directement des données. Bien que produisant déjà des résultats impressionnants dans beaucoup de domaines tels que la reconnaissance de la parole, la vision par ordinateur ou encore la traduction automatique, il y a encore de nombreux défis dans l'entraînement et dans le déploiement des réseaux de neurones. En particulier, entraîner des réseaux de neurones nécessite typiquement d'énormes ressources computationnelles, et les modèles entraînés sont souvent trop gros ou trop gourmands en ressources pour être déployés sur des appareils dont les ressources sont limitées, tels que les téléphones intelligents ou les puces de faible puissance. Les articles présentés dans cette thèse étudient des solutions à ces différents problèmes. Les deux premiers articles se concentrent sur l'amélioration de l'entraînement des réseaux de neurones récurrents (RNNs), un type de réseaux de neurones particulier conçu pour traiter des données séquentielles. Les RNNs sont notoirement difficiles à entraîner, donc nous proposons d'améliorer leur paramétrisation en y intégrant la normalisation par lots (BN), qui était jusqu'à lors uniquement appliquée aux réseaux non-récurrents. Dans le premier article, nous appliquons BN aux connections des entrées vers les couches cachées du RNN, ce qui réduit le décalage covariable entre les différentes couches; et dans le second article, nous montrons comment appliquer BN aux connections des entrées vers les couches cachées et aussi des couches cachée vers les couches cachée des réseau récurrents à mémoire court et long terme (LSTM), une architecture populaire de RNN, ce qui réduit également le décalage covariable entre les pas de temps. Nos expériences montrent que les paramétrisations proposées permettent d'entraîner plus rapidement et plus efficacement les RNNs, et ce sur différents bancs de tests. Dans le troisième article, nous proposons un nouvel optimiseur pour accélérer l'entraînement des réseaux de neurones. Les optimiseurs diagonaux traditionnels, tels que RMSProp, opèrent dans l'espace des paramètres, ce qui n'est pas optimal lorsque plusieurs paramètres sont mis à jour en même temps. A la place, nous proposons d'appliquer de tels optimiseurs dans une base dans laquelle l'approximation diagonale est susceptible d'être plus efficace. Nous tirons parti de l'approximation K-FAC pour construire efficacement cette base propre Kronecker-factorisée (KFE). Nos expériences montrent une amélioration en vitesse d'entraînement par rapport à K-FAC, et ce pour différentes architectures de réseaux de neurones profonds. Le dernier article se concentre sur la taille des réseaux de neurones, i.e. l'action d'enlever des paramètres du réseau, afin de réduire son empreinte mémoire et son coût computationnel. Les méthodes de taille typique se base sur une approximation de Taylor de premier ou de second ordre de la fonction de coût, afin d'identifier quels paramètres peuvent être supprimés. Nous proposons d'étudier l'impact des hypothèses qui se cachent derrière ces approximations. Aussi, nous comparons systématiquement les méthodes basées sur des approximations de premier et de second ordre avec la taille par magnitude (MP), et montrons comment elles fonctionnent à la fois avant, mais aussi après une phase de réapprentissage. Nos expériences montrent que mieux préserver la fonction de coût ne transfère pas forcément à des réseaux qui performent mieux après la phase de réapprentissage, ce qui suggère que considérer uniquement l'impact de la taille sur la fonction de coût ne semble pas être un objectif suffisant pour développer des bon critères de taille.Neural networks are a family of Machine Learning models able to learn complex tasks directly from the data. Although already producing impressive results in many areas such as speech recognition, computer vision or machine translation, there are still a lot of challenges in both training and deployment of neural networks. In particular, training neural networks typically requires huge amounts of computational resources, and trained models are often too big or too computationally expensive to be deployed on resource-limited devices, such as smartphones or low-power chips. The articles presented in this thesis investigate solutions to these different issues. The first couple of articles focus on improving the training of Recurrent Neural Networks (RNNs), networks specially designed to process sequential data. RNNs are notoriously hard to train, so we propose to improve their parameterisation by upgrading them with Batch Normalisation (BN), a very effective parameterisation which was hitherto used only in feed-forward networks. In the first article, we apply BN to the input-to-hidden connections of the RNNs, thereby reducing internal covariate shift between layers. In the second article, we show how to apply it to both input-to-hidden and hidden-to-hidden connections of the Long Short-Term Memory (LSTM), a popular RNN architecture, thus also reducing internal covariate shift between time steps. Our experiments show that these proposed parameterisations allow for faster and better training of RNNs on several benchmarks. In the third article, we propose a new optimiser to accelerate the training of neural networks. Traditional diagonal optimisers, such as RMSProp, operate in parameters coordinates, which is not optimal when several parameters are updated at the same time. Instead, we propose to apply such optimisers in a basis in which the diagonal approximation is likely to be more effective. We leverage the same approximation used in Kronecker-factored Approximate Curvature (K-FAC) to efficiently build this Kronecker-factored Eigenbasis (KFE). Our experiments show improvements over K-FAC in training speed for several deep network architectures. The last article focuses on network pruning, the action of removing parameters from the network, in order to reduce its memory footprint and computational cost. Typical pruning methods rely on first or second order Taylor approximations of the loss landscape to identify which parameters can be discarded. We propose to study the impact of the assumptions behind such approximations. Moreover, we systematically compare methods based on first and second order approximations with Magnitude Pruning (MP), showing how they perform both before and after a fine-tuning phase. Our experiments show that better preserving the original network function does not necessarily transfer to better performing networks after fine-tuning, suggesting that only considering the impact of pruning on the loss might not be a sufficient objective to design good pruning criteria

Iz časopisa

From the field of cartography and geoinformation, there are journal’s article extracts given which are not cartographic first and whose complete texts are on the Internet, accessible to the members of Croatian academic and research community. Most journals can be accessed through the PERO browser (http://knjiznica.irb.hr/pero/index.php). For the journals not found through this browser, the complete texts of the mentioned articles are available for free on the given web-address. Next to every jo­urnal headline, in the brackets, it is noted which prominent bibliographic and quotation bases it is placed in: CC (Current Contents), SCIE (Science Citati­­on Index Expanded), and SSCI (Social Sc­­­i­ence Citation Index). It should be noted that, for some journals accessible through PERO browser, there is a delay of 6, 12 and even 18 months in accessing the newest issues. This number is given in the brackets next to the journal’s headline.Bullettin of the GSI (Geospatial Information Authority of Japan)http://www.gsi.go.jp/ENGLISH/page_e30092.htmlK. Kawase: A general formula for calculating meridian arc length and its application to coordinate conversion in the Gauss-Krüger projection, Vol. 59, December 2011.K. Kawase: Concise derivation of extensive coordinate conversion formulae in the Gauss-Krüger pro­jection, Vol. 60, December 2012.Coordinates (A monthly magazine on positioning, navigation and beyond) http://mycoordinates.orgT. Nagayama, K. Inaba, T. Hayashi, H: Nakai: Responding to the great east Japan earthquake, 2012, 12.J. SF Fabic: Data integration and sharing for disaster management, 2012, 12.D. Ampatzidis: Datum transformations using exclusively geodetic curvilinear coordinates without height information, 2012, 12.Geomatics and Environmental Engineeringhttp://journals.bg.agh.edu.pl/GEOMATICS/index.phpR. Cellmer, A. Senetra, A. Szczepanska: Land value maps of naturally valuable areas, 2012, 3.Geopolitics (CC, SSCI) (12)J. Strandsbjerg: Cartopolitics, geopolitics and boundaries in the Arctic, 2012, 4.International Journal of Geographical Information Science (CC, SCIE, SSCI) (12)H. Fan, L. Meng: A three-step approach of simplifying 3D buildings modeled by CityGML, 2012, 6.D. Hardy, J. Frew, M. F. Goodchild: Volunteered geographic information production as a spatial process, 2012, 7.P. Taillandier, J. Gaffuri: Improving map generalisation with new pruning heuristics, 2012, 7.ISPRS International Journal of Geo-Informationhttp://www.mdpi.com/journal/ijgiP. Neis, A. Zipf: Analyzing the contributor activity of a volunteered geographic information project — The case of OpenStreetMap, 2012, 2.P. Neis, M. Goetz, A. Zipf: Towards automatic vandalism detection in OpenStreetMap, 2012, 3.ISPRS Journal of Photogrammetry and Remote Sensing (CC, SCIE)http://www.sciencedirect.com/science/journal/09242716J-H. Haunert: A symmetry detector for map generalization and urban-space analysis, Vol. 74, November 2012.Journal of Historical Geography (CC, SSC) (12)D. Fedman, C. Karacas: A cartographic fade to black: mapping the destruction of urban Japan during World War II, 2012, 3.M. Yilmaz: Historical mosque orientation in Turkey: Central-Western Anatolia Region, 1150‒1590, 2012, 4.Landscape Ecology (CC, SCIE)http://link.springer.com/journal/10980J. Liang: Mapping large-scale forest dynamics: a geospatial approach, 2012, 8.Naše morehttp://hrcak.srce.hr/nase-moreI. Pavić: Geografsko-informacijski sus­tav i model razvoja pomorskoga ka­tastra, 2012, 5-6.Remote Sensing of Environment (CC, SCIE)N. Levin, A. Heimowitz: Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS, Vol. 126 November 2012.Tehnički vjesnik (SCIE)http://hrcak.srce.hr/tehnicki-vjesnik R. Župan, D. Sruk, S. Frangeš: Experiment for determination of map graphics segment standard for handheld crisis maps management, 2012, 4.URISA Journalhttp://www.urisa.org/PSS_journal_archivesM. Martin, B. Peters, J. Corbett: Participatory asset mapping in the Lake Victoria Basin of Kenya, 2012, 2.P. A. Johnson, R. E. Sieber: Increasing access to and use of geospatial data by municipal government and citizens: the process of “Geomatization” in rural Québec, 2012, 2.A. Poplin: Web-based PPGIS for Wilhelmsburg, Germany: An integration of interactive GIS-based maps with an online questionnaire, 2012, 2. Dan je izbor članaka iz područja kartografije i geoinformacija iz časopisa, koji nisu u prvom redu kartografski, a kojima su cjeloviti tekstovi dostupni na internetu članovima hrvatske akademske i istraživačke zajednice. Većina časopisa dostupna je preko pretraživača PERO (http:// knjiznica.irb.hr/pero/index.php). Za časopise koji nisu dostupni preko tog pretraživača cjeloviti tekstovi navedenih članaka slobodno su pristupačni na upisanoj web-adresi. Uz svaki je časopis u zagradi naznačeno u koje je ugledne bibliografske i citatne baze uvršten: CC (Current Contents), SCIE (Science Citation Index Expanded), SSCI (Social Science Citation Index). Treba naglasiti da za neke časopise, dostupne preko pretraživača PERO, postoji odgoda pristupa najnovijim brojevima od 6, 12, a ponekad i 18 mjeseci. Taj broj je naveden u zagradi uz naslov časopisa. Bullettin of the GSI (Geospatial Information Authority of Japan)http://www.gsi.go.jp/ENGLISH/page_e30092.htmlK. Kawase: A general formula for calculating meridian arc length and its application to coordinate conversion in the Gauss-Krüger projection, Vol. 59, December 2011.K. Kawase: Concise derivation of extensive coordinate conversion formulae in the Gauss-Krüger pro­jection, Vol. 60, December 2012.Coordinates (A monthly magazine on positioning, navigation and beyond) http://mycoordinates.orgT. Nagayama, K. Inaba, T. Hayashi, H: Nakai: Responding to the great east Japan earthquake, 2012, 12.J. SF Fabic: Data integration and sharing for disaster management, 2012, 12.D. Ampatzidis: Datum transformations using exclusively geodetic curvilinear coordinates without height information, 2012, 12.Geomatics and Environmental Engineeringhttp://journals.bg.agh.edu.pl/GEOMATICS/index.phpR. Cellmer, A. Senetra, A. Szczepanska: Land value maps of naturally valuable areas, 2012, 3.Geopolitics (CC, SSCI) (12)J. Strandsbjerg: Cartopolitics, geopolitics and boundaries in the Arctic, 2012, 4.International Journal of Geographical Information Science (CC, SCIE, SSCI) (12)H. Fan, L. Meng: A three-step approach of simplifying 3D buildings modeled by CityGML, 2012, 6.D. Hardy, J. Frew, M. F. Goodchild: Volunteered geographic information production as a spatial process, 2012, 7.P. Taillandier, J. Gaffuri: Improving map generalisation with new pruning heuristics, 2012, 7.ISPRS International Journal of Geo-Informationhttp://www.mdpi.com/journal/ijgiP. Neis, A. Zipf: Analyzing the contributor activity of a volunteered geographic information project — The case of OpenStreetMap, 2012, 2.P. Neis, M. Goetz, A. Zipf: Towards automatic vandalism detection in OpenStreetMap, 2012, 3.ISPRS Journal of Photogrammetry and Remote Sensing (CC, SCIE)http://www.sciencedirect.com/science/journal/09242716J-H. Haunert: A symmetry detector for map generalization and urban-space analysis, Vol. 74, November 2012.Journal of Historical Geography (CC, SSC) (12)D. Fedman, C. Karacas: A cartographic fade to black: mapping the destruction of urban Japan during World War II, 2012, 3.M. Yilmaz: Historical mosque orientation in Turkey: Central-Western Anatolia Region, 1150‒1590, 2012, 4.Landscape Ecology (CC, SCIE)http://link.springer.com/journal/10980J. Liang: Mapping large-scale forest dynamics: a geospatial approach, 2012, 8.Naše morehttp://hrcak.srce.hr/nase-moreI. Pavić: Geografsko-informacijski sus­tav i model razvoja pomorskoga ka­tastra, 2012, 5-6.Remote Sensing of Environment (CC, SCIE)N. Levin, A. Heimowitz: Mapping spatial and temporal patterns of Mediterranean wildfires from MODIS, Vol. 126 November 2012.Tehnički vjesnik (SCIE)http://hrcak.srce.hr/tehnicki-vjesnikR. Župan, D. Sruk, S. Frangeš: Experiment for determination of map graphics segment standard for handheld crisis maps management, 2012, 4.URISA Journalhttp://www.urisa.org/PSS_journal_archivesM. Martin, B. Peters, J. Corbett: Participatory asset mapping in the Lake Victoria Basin of Kenya, 2012, 2.P. A. Johnson, R. E. Sieber: Increasing access to and use of geospatial data by municipal government and citizens: the process of “Geomatization” in rural Québec, 2012, 2.A. Poplin: Web-based PPGIS for Wilhelmsburg, Germany: An integration of interactive GIS-based maps with an online questionnaire, 2012, 2.

Generating High Precision Classification Rules for Screening of Irrelevant Studies in Systematic Review Literature Searches

Systematic reviews aim to produce repeatable, unbiased, and comprehensive answers to clinical questions. Systematic reviews are an essential component of modern evidence based medicine, however due to the risks of omitting relevant research they are highly time consuming to create and are largely conducted manually. This thesis presents a novel framework for partial automation of systematic review literature searches. We exploit the ubiquitous multi-stage screening process by training the classifier using annotations made by reviewers in previous screening stages. Our approach has the benefit of integrating seamlessly with the existing screening process, minimising disruption to users. Ideally, classification models for systematic reviews should be easily interpretable by users. We propose a novel, rule based algorithm for use with our framework. A new approach for identifying redundant associations when generating rules is also presented. The proposed approach to redundancy seeks to both exclude redundant specialisations of existing rules (those with additional terms in their antecedent), as well as redundant generalisations (those with fewer terms in their antecedent). We demonstrate the ability of the proposed approach to improve the usability of the generated rules. The proposed rule based algorithm is evaluated by simulated application to several existing systematic reviews. Workload savings of up to 10% are demonstrated. There is an increasing demand for systematic reviews related to a variety of clinical disciplines, such as diagnosis. We examine reviews of diagnosis and contrast them against more traditional systematic reviews of treatment. We demonstrate existing challenges such as target class heterogeneity and high data imbalance are even more pronounced for this class of reviews. The described algorithm accounts for this by seeking to label subsets of non-relevant studies with high precision, avoiding the need to generate a high recall model of the minority class

Learning Domain-Independent Planning Heuristics with Hypergraph Networks

We present the first approach capable of learning domain-independent planning heuristics entirely from scratch. The heuristics we learn map the hypergraph representation of the delete-relaxation of the planning problem at hand, to a cost estimate that approximates that of the least-cost path from the current state to the goal through the hypergraph. We generalise Graph Networks to obtain a new framework for learning over hypergraphs, which we specialise to learn planning heuristics by training over state/value pairs obtained from optimal cost plans. Our experiments show that the resulting architecture, STRIPS-HGNs, is capable of learning heuristics that are competitive with existing delete-relaxation heuristics including LM-cut. We show that the heuristics we learn are able to generalise across different problems and domains, including to domains that were not seen during training

Finding and using analogies to guide mathematical proof

This thesis is concerned with reasoning by analogy within the context of auto-mated problem solving. In particular, we consider the provision of an analogical reasoning component to a resolution theorem proving system. The framework for reasoning by analogy which we use (called Basic APS) contains three major components -the finding of analogies (analogy matching), the construction of analogical plans, and the application of the plans to guide the search of a theorem prover. We first discuss the relationship of analogy to other machine learning techniques. We then develop programs for each of the component processes of Basic APS.First we consider analogy matching. We reconstruct, analyse and crticise two previous analogy matchers. We introduce the notion of analogy heuristics in order to understand the matchers. We find that we can explain the short-comings of the matchers in terms of analogy heuristics. We then develop a new analogy matching algorithm, based on flexible application of analogy heuristics, and demonstrate its superiority to the previous matchers.We go on to consider analogical plan construction. We describe procedures for constructing a plan for the solution of a problem, given the solution of a different problem and an analogy match between the two problems. Again, we compare our procedures with corresponding ones from previous systems.We then describe procedures for the execution of analogical plans. We demon-strate the procedures on a number of example analogies. The analogies involved are straightforward for a human, but the problems themselves involve.huge search spaees, if tackled directly using resolution. By comparison with unguided search, we demonstrate the dramatic reductfon in search entaile_d by the use of an ana-logical plan.We then consider some directions for development of our analogy systems, which have not yet been implemented. Firstly, towards more flexible and power-ful execution of analogical plans. Secondly, towards an analogy system which can improve its own ability to find and apply analogies over the course of experience

An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images

Artificial neural networks (ANNs) have attracted the attention of researchers in many fields, and have been used to solve a wide range of problems. In the field of remote sensing they have been used in a variety of applications, including land cover mapping, image compression, geological mapping and meteorological image classification, and have generally proved to be more powerful than conventional statistical classifiers, especially when training data are limited and the data in each class are not normally distributed. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the accuracy of the resulting classification. Although there are some discussions in the literature regarding the issues that affect network performance, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. In this thesis, a feed-forward network structure that learns the characteristics of the training data through the backpropagation learning algorithm is employed to classify land cover features using multispectral, multitemporal, and multisensory image data. The thesis starts with a review and discussion of general principles of classification and the use of artificial neural networks. Special emphasis is put on the issue of feature selection, due to the availability of hyperspectral image data from recent sensors. The primary aims of this research are to comprehensively investigate the impact of the choice of network architecture and initial parameter estimates, and to compare a number of heuristics developed by researchers. The most effective heuristics are identified on the basis of a large number of experiments employing two real-world datasets, and the superiority of the optimum settings using the 'best' heuristics is then validated using an independent dataset. The results are found to be promising in terms of ease of design and use of ANNs, and in producing considerably higher classification accuracies than either the maximum likelihood or neural network classifiers constructed using ad hoc design and implementation strategies. A number of conclusions are drawn and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of artificial neural networks in remote sensing image classification. This study also explores the use of visualisation techniques in understanding the behaviour of artificial neural networks and the results produced by them. A number of visual analysis techniques are employed to examine the internal characteristics of the training data. For this purpose, a toolkit allowing the analyst to perform a variety of visualisation and analysis procedures was created using the MATLAB software package, and is available in the accompanying CD-ROM. This package was developed during the course of this research, and contains the tools used during the investigations reported in this thesis. The contribution to knowledge of the research work reported in this thesis lies in the identification of optimal strategies for the use of ANNs in land cover classifications based on remotely sensed data. Further contributions include an indepth analysis of feature selection methods for use with high-dimensional datasets, and the production of a MATLAB toolkit that implements the methods used in this study

Feed forward neural networks and genetic algorithms for automated financial time series modelling

This thesis presents an automated system for financial time series modelling. Formal and applied methods are investigated for combining feed-forward Neural Networks and Genetic Algorithms (GAs) into a single adaptive/learning system for automated time series forecasting. Four important research contributions arise from this investigation: i) novel forms of GAs are introduced which are designed to counter the representational bias associated with the conventional Holland GA, ii) an experimental methodology for validating neural network architecture design strategies is introduced, iii) a new method for network pruning is introduced, and iv) an automated method for inferring network complexity for a given learning task is devised. These methods provide a general-purpose applied methodology for developing neural network applications and are tested in the construction of an automated system for financial time series modelling. Traditional economic theory has held that financial price series are random. The lack of a priori models on which to base a computational solution for financial modelling provides one of the hardest tests of adaptive system technology. It is shown that the system developed in this thesis isolates a deterministic signal within a Gilt Futures prices series, to a confidences level of over 99%, yielding a prediction accuracy of over 60% on a single run of 1000 out-of-sample experiments. An important research issue in the use of feed-forward neural networks is the problems associated with parameterisation so as to ensure good generalisation. This thesis conducts a detailed examination of this issue. A novel demonstration of a network's ability to act as a universal functional approximator for finite data sets is given. This supplies an explicit formula for setting a network's architecture and weights in order to map a finite data set to arbitrary precision. It is shown that a network's ability to generalise is extremely sensitive to many parameter choices and that unless careful safeguards are included in the experimental procedure over-fitting can occur. This thesis concentrates on developing automated techniques so as to tackle these problems. Techniques for using GAs to parameterise neural networks are examined. It is shown that the relationship between the fitness function, the GA operators and the choice of encoding are all instrumental in determining the likely success of the GA search. To address this issue a new style of GA is introduced which uses multiple encodings in the course of a run. These are shown to out-perform the Holland GA on a range of standard test functions. Despite this innovation it is argued that the direct use of GAs to neural network parameterisation runs the risk of compounding the network sensitivity issue. Moreover, in the absence of a precise formulation of generalisation a less direct use of GAs to network parameterisation is examined. Specifically a technique, artficia1 network generation (ANG), is introduced in which a GA is used to artificially generate test learning problems for neural networks that have known network solutions. ANG provides a means for directly testing i) a neural net architecture, ii) a neural net training process, and iii) a neural net validation procedure, against generalisation. ANG is used to provide statistical evidence in favour of Occam's Razor as a neural network design principle. A new method for pruning and inferring network complexity for a given learning problem is introduced. Network Regression Pruning (NRP) is a network pruning method that attempts to derive an optimal network architecture by starting from what is considered an overly large network. NRP differs radically from conventional pruning methods in that it attempts to hold a trained network's mapping fixed as pruning proceeds. NRP is shown to be extremely successful at isolating optimal network architectures on a range of test problems generated using ANG. Finally, NRP and techniques validated using ANG are combined to implement an Automated Neural network Time series Analysis System (ANTAS). ANTAS is applied to the gilt futures price series The Long Gilt Futures Contract (LGFC)