Ken Sharman

Nonlinear model structure identification using genetic programming

Genetic Programming is an optimisation procedure which may be applied to the identification of the nonlinear structure of a dynamic model from experimental data. In such applications, the model structure may be described either by differential equations or by a block diagram and the algorithm is configured to minimise the sum of the squares of the error between the recorded experimental response from the real system and the corresponding simulation model output. The technique has been applied successfully to the modelling of a laboratory scale process involving a coupled water tank system and to the identification of a helicopter rotor speed controller and engine from flight test data. The resulting models provide useful physical insight.

Genetic algorithm automated approach to the design of sliding mode control systems

Although various nonlinear control theories, such as sliding mode control, have proved sound and successful, there is a serious lack of effective or tractable design methodologies due to difficulties encountered in the application of traditional analytical and numerical methods. This paper develops a reusable computing paradigm based on genetic algorithms to transform the ‘unsolvable problem’ of optimal designs into a practically solvable ‘non-deterministic polynomial problem’, which results in computer automated designs directly from nonlinear plants. The design methodology takes into account practical system constraints and extends the solution space, allowing new control terms to be included in the controller structure. In addition, the practical implementations using laboratory-scale systems demonstrate that such ‘off-the-computer’ designs offer a superior performance to manual designs in terms of transient and steady-state responses and of robustness. Various contributions to the genetic algorithm te...

On Harnessing the Electroencephalogram for the Musical Braincap

The braincap, as described in 3001: The Final Odyssey, the concluding edition of Arthur C. Clarke’s science fiction classic, is the ultimate humancomputer interface: it connects the brain to a system that is able to read thoughts and upload new information. The wearer can in minutes acquire new skills that would otherwise take years to master. Currently, however, a system that uploads information into the brain cannot exist outside the realm of science fiction, although machines that can read signals from the brain are becoming present-day reality. Furthermore, we should soon be able to control all sorts of devices by our thoughts alone. In 1998, a paper presented at the 9th European Congress of Clinical Neurophysiology already reported impressive advances in research on an electroencephalogram-based system to control a prosthetic hand (Guger and Pfurtscheller 1998). More recently, scientists at Brown University reported the development of a brain-computer interface for a system whereby a monkey controlled a cursor on a computer screen (Turner 2002). At first, the monkey used a joystick to move the cursor. After a while, the joystick was disconnected, and the monkey, who had not realized this, continued moving the cursor by means of tiny electrical signals emanating from an electrode implanted on the monkey’s motor cortex (the main brain area for motor control). We are interested in developing thoughtcontrolled musical devices, and to this end we are currently working on the design of a musical braincap. We are developing technology to interface the brain with music systems and compositional techniques suitable for thought control. This article focuses on extracting and harnessing tiny electrical brain signals from electroencephalograms (EEGs) that can be captured with electrodes on the scalp. We present three experiments whose results provide the basis for building systems to automatically detect information in the electroencephalogram associated with musical mental activities. Then, we describe how these results are currently being embedded in the design of the musical braincap. Before we present the experiments, we briefly introduce the growing field of BrainComputer Interfaces (BCI), followed by an introduction to the EEG and the signal processing techniques we employed to harness it. Before we continue, it is necessary to clarify the meaning of the expression ‘‘thought control.’’ In Eduardo Reck Miranda,* Ken Sharman,† Kerry Kilborn,‡ and Alexander Duncan§ *Computer Music Research—Neuroscience of Music Group, School of Computing, Communications, and Electronics, University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, United Kingdom eduardo.miranda@plymouth.ac.uk † Instituto Tecnologico de Informatica Universidad Politecnica de Valencia, Camino de Vera s/n, 46071 Valencia, Spain ken@iti.upv.es ‡ Department of Psychology University of Glasgow, 58 Hillhead Street, Glasgow G12 8QB, United Kingdom k.kilborn@psy.gla.ac.uk § The Sun Centre Prades, 48160 St. Martin de Boubaux, France alex@thesuncentre.net On Harnessing the Electroencephalogram for the Musical Braincap

Bloat control operators and diversity in genetic programming: A comparative study

This paper reports a comparison of several bloat control methods and also evaluates a recent proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to test the adequacy of this method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains, prune and plant has demonstrated to be better in terms of fitness, size reduction, and time consumption than any of the other bloat control techniques under comparison. The experimental part of the study presents a comparison of performance in terms of phenotypic and genotypic diversity. This comparison study can provide the practitioner with some relevant clues as to which bloat control method is better suited to a particular problem and whether the advantage of a method does or does not derive from its influence on the genetic pool diversity.

A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database

In this paper we present the application of a genetic programming algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database of Spanish companies. The database has two important drawbacks: the number of bankrupt companies is very small when compared with the number of healthy ones (unbalanced data) and a considerable number of companies have missing data. For comparison purposes we have solved the same problem using a support vector machine. Genetic programming has achieved very satisfactory results, improving those obtained with the support vector machine.

Testing the Intermediate Disturbance Hypothesis: Effect of Asynchronous Population Incorporation on Multi-Deme Evolutionary Algorithms

In P2P and volunteer computing environments, resources are not always available from the beginning to the end, getting incorporated into the experiment at any moment. Determining the best way of using these resources so that the exploration/exploitation balance is kept and used to its best effect is an important issue. The Intermediate Disturbance Hypothesis states that a moderate population disturbance (in any sense that could affect the population fitness) results in the maximum ecological diversity. In the line of this hypothesis, we will test the effect of incorporation of a second population in a two-population experiment. Experiments performed on two combinatorial optimization problems, MMDP and P-Peaks , show that the highest algorithmic effect is produced if it is done in the middle of the evolution of the first population; starting them at the same time or towards the end yields no improvement or an increase in the number of evaluations needed to reach a solution. This effect is explained in the paper, and ascribed to the intermediate disturbanceproduced by first-population immigrants in the second population.

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.

Prune and Plant: A New Bloat Control Method for Genetic Programming

This paper reports a comparison of several bloat control methods and also evaluates a new proposal for limiting the size of the individuals: a genetic operator called prune and plant. The aim of this work is to prove the adequacy of this new method. Since a preliminary study of the method has already shown promising results, we have performed a thorough study in a set of benchmark problems aiming at demonstrating the utility of the new approach. Prune and plant has obtained results that maintain the quality of the final solutions in terms of fitness while achieving a substantial reduction of the mean tree size in all four problem domains considered. In addition, in one of these problem domains prune and plant has demonstrated to be better in terms of fitness, size reduction and time consumption than any of the other bloat control techniques under comparison.

Strong Typing, Variable Reduction and Bloat Control for Solving the Bankruptcy Prediction Problem Using Genetic Programming

In this chapter we present the application of a genetic programming (GP) algorithm to the problem of bankruptcy prediction. To carry out the research we have used a database that includes extensive information (not only economic) from the companies. In order to handle the different data types we have used Strongly Typed GP and variable reduction. Also, bloat control has been implemented to obtain comprehensible classification models. For comparison purposes we have solved the same problem using a support vector machine (SVM). GP has achieved very satisfactory results, improving those obtained with the SVM.

Modeling Pheromone Dispensers Using Genetic Programming

Mating disruption is an agricultural technique that intends to substitute the use of insecticides for pest control. This technique consists of the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted. Pheromones are released using devices called dispensers. The speed of release is, generally, a function of time and atmospheric conditions such as temperature and humidity. One of the objectives in the design of the dispensers is to minimise the effect of atmospheric conditions in the performance of the dispenser. With this objective, the Centro de Ecologia Quimica Agricola (CEQA) has designed an experimental dispenser that aims to compete with the dispensers already in the market. The hypothesis we want to validate (and which is based on experimental results) is that the performance of the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus. This was done using a genetic programming (GP) algorithm. GP evolved functions able to describe the performance of both dispensers and that support the initial hypothesis.