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Optical fibres are not only one of the major components of modern optical communications systems, but are also used in other areas such as sensing, medicine and optical filtering. Silica microstructured optical fibres are a type of optical fibre where microscopic holes within the fibre result in highly tailorable optical properties, which are not possible in traditional fibres. Microstructured fibres manufactured from polymer, instead of silica, are a relatively recent development in optical fibre technology, and support a wide variety of microstructure fibre geometries, when compared to the more commonly used silica. In order to meet the automated design requirements for such complex fibres, a representation was developed which can describe radially symmetric microstructured fibres of different complexities; from simple hexagonal designs with very few holes, to large arrays of hundreds of holes. This chapter presents a genetic algorithm which uses an embryogeny representation, or a growth phase, to convert a design from its genetic encoding (genotype) to the microstructured fibre (phenotype). The work demonstrates the application of variable-complexity, evolutionary design approaches to photonic design. The inclusion of real-world constraints within the embryogeny aids in the manufacture of designs, resulting in the physical construction and experimental characterisation of both single-mode and highbandwidth multi-mode microstructured fibres, where some GA-designed fibres are currently being patented. [ABSTRACT FROM AUTHOR]

In this chapter, we discuss how to optimize a grocery store's performance using personalized pricing and evolutionary computation. Currently most of the grocery stores provide special discounts to their customers under different loyalty card programs. However, since each individual's shopping behavior is not taken into consideration, these discounts do not help optimize the store performance. We believe that a more determined approach such as individual pricing could enable retailers to optimize their store performance by giving special discounts to each customer. The objective here is to determine the feasibility of individual pricing to optimize the store performance and compare it against the traditional product-centered approach. Each customer is modeled as an agent and his/her shopping behavior is obtained from transaction data. Then, the overall shopping behavior is simulated and the store performance is optimized using Monte-Carlo simulations and evolutionary computation. The results showed that individual pricing outperforms the traditional product-centered approach significantly. We believe that the successful implementation of the proposed research will impact the grocery retail significantly by increasing customer satisfaction and profits. [ABSTRACT FROM AUTHOR]

This chapter describes interactive evolutionary design, a powerful technique where one marries the exploratory capabilities of evolutionary computation with the esthetic skills and sensibility of the human as selective agent. Interactive evolutionary design has the potential to be an enormously useful tool to graphic designers. However, in order for it to become commonly used, a number of barriers to use must be overcome, including making it both simpler to understand (e.g., more intuitive genotype-phenotype mappings) and with greater user-control (e.g., allow the user to lock "perfect" elements of a design). In this study we explore these ideas—how can one make interactive evolutionary design more appealing, useful and practical?—by developing a design tool "evoDesign" that employs a genetic algorithm to evolve designs. As an illustrative test case, evoDesign is used to evolve tiles used for walls or floors or as a repeating unit for fabrics and wallpapers. [ABSTRACT FROM AUTHOR]

North American Air Liquide has implemented several innovative applications of evolutionary computation and ant colony optimization. This chapter describes Air Liquide's operations and the two major areas in which optimization has significantly impacted Air Liquide: production and distribution of liquid industrial gases, and production and distribution of industrial gases via dual pipelines. [ABSTRACT FROM AUTHOR]

This chapter is a nontechnical discussion of 12 lessons learned over the course of 25 years of applications of evolutionary computation in industry. The lessons bear on project assessment and planning, project management, project interfaces, and managing change. [ABSTRACT FROM AUTHOR]

To assist in understanding trends in the field of Evolutionary Computation (EC) and in helping graduates find jobs in EC, we conducted a survey from March 2005 to February 2006 on members of the EC community. The analysis reveals various technology transfer strategies and activities took place during the past 50 years: parallel exploration of multiple application areas; a combination of exploitation and exploration approaches to develop EC applications; and the healthy migration of EC practitioners between different parts of the globe.We believe these emerged and self-organized phenomena contribute to the growth of the field. While there are still challenges in deploying evolutionary computation to industry in a grand scale, the EC community demonstrates the adaptability and resilience necessary to achieve that goal. [ABSTRACT FROM AUTHOR]

High quality and innovation are major selling points in the technology market. Continuous improvement of products and the introduction of completely new products are a day to day challenge that industry has to face to keep competitive in a dynamic market. Customers desire changes when new materials and technologies become available. Consequently, new production views such as the whole life cycle cost of a product become an issue in industry. Keeping up with these changes is difficult and the application of the most recent technologies in a sound and effective way is often not straight forward. Academia is one of the sources of novel and scientifically well founded technologies. Furthermore, academia has a rich pool of thoroughly tested methods, well educated students and professional academics to deliver these methods. Technology transfer between academia and industry, therefore, is a productive way to bridge the gap between ‘mysterious' theory and ‘plain' practice. Various aspects of this transfer are discussed in this chapter. The most recent technology of multi-objective optimization is introduced to illustrate the challenges that come along with the cooperation between academia and industry. [ABSTRACT FROM AUTHOR]

Evolutionary computation has created significant value in the chemical industry by improving manufacturing processes and accelerating new product discovery. The key competitive advantages of evolutionary computation, based on industrial applications in the chemical industry are defined as: no a priori modeling assumptions, high quality empirical models, easy integration in existing industrial work processes, minimal training of the final user, and low total cost of development, deployment, and maintenance. An overview of the key technical, organizational, and political issues that need to be resolved for successful application of EC in industry is given in the chapter. Examples of successful application areas are: inferential sensors, empirical emulators of mechanistic models, accelerated new product development, complex process optimization, effective industrial design of experiments, and spectroscopy. [ABSTRACT FROM AUTHOR]

Resource based scheduling using permutation based representations is reviewed. Permutation based representations are used in conjunction with genetic algorithms and local search algorithms for solving three very different scheduling problems. First, the Coors warehouse scheduling problem involves finding a permutation of customer orders that minimizes the average time that customers' orders spend at the loading docks while at the same time minimizing the running average inventory. Second, scheduling the Air Force Satellite Control Network (AFSCN) involves scheduling customer requests for contact time with a satellite via a ground station, where slot times on a ground station is the limited resource. The third application is scheduling the tracking of objects in space using ground based radar systems. Both satellites and debris in space must be tracked on regular basis to maintain knowledge about the location and orbit of the object. The ground based radar system is the limited resource, but unlike AFSCN scheduling, this application involves significant uncertainty. [ABSTRACT FROM AUTHOR]

Well log data are routinely used for stratigraphic interpretation of the earth's subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology. [ABSTRACT FROM AUTHOR]

Railway track intervention planning is the process of specifying the location and time of required maintenance and renewal activities. To facilitate the process, decision support tools have been developed and typically use an expert system built with rules specified by track maintenance engineers. However, due to the complex interrelated nature of component deterioration, it is problematic for an engineer to consider all combinations of possible deterioration mechanisms using a rule based approach. To address this issue, this chapter describes an approach to the intervention planning using a variety of computational intelligence techniques. The proposed system learns rules for maintenance planning from historical data and incorporates future data to update the rules as they become available thus the performance of the system improves over time. To determine the failure type, historical deterioration patterns of sections of track are first analyzed. A Rival Penalized Competitive Learning algorithm is then used to determine possible failure types. We have devised a generalized two stage evolutionary algorithm to produce curve functions for this purpose. The approach is illustrated using an example with real data which demonstrates that the proposed methodology is suitable and effective for the task in hand. [ABSTRACT FROM AUTHOR]

The representation of a design influences the success of any kind of optimization significantly. The perfect trade-off between the number of parameters which define the search space and the achievable design flexibility is very crucial since it influences the convergence speed of the chosen optimization algorithm as well as the possibility to find the design which provides the best performance. Classical methods mostly define the design directly, e.g. via spline surfaces or by representations which are specialized to one design task. In the present chapter, the so-called deformation methods are focused which follow a different approach. Instead of describing the shape directly, deformation terms are used to morph an initial design into newones. This decouples a complex design from an expensive shape description while relying purely on mapping terms which are responsible for the geometry transformations. Thus, the designer is encouraged to determine the optimal relation between parameter set and design flexibility according to the given task. With respect to the optimization, these mapping terms are considered as parameters. In this chapter, the combination of two state of the art deformation algorithms with evolutionary optimization is focused. After an introduction of these techniques, a framework for an autonomous design optimization is sketched in more detail. By means of two optimizations, which feature a stator blade of a jet turbine the workability is shown and the advantages of such representations are highlighted. [ABSTRACT FROM AUTHOR]

This chapter discusses an application of multi-objective genetic algorithm for designing products with a built-in disassembly means that can be triggered by the removal of one or a few fasteners at the end of the product lives. Given component geometries, the method simultaneously determines the spatial configuration of components, locators and fasteners, and the end-of-life (EOL) treatments of components and subassemblies, such that the product can be disassembled for the maxim profit and minimum environmental impact through recycling and reuse via domino-like "self-disassembly" process. A multi-objective genetic algorithm is utilized to search for Pareto optimal designs in terms of 1) satisfaction of the distance specification among components, 2) efficient use of locators on components, 3) profit of EOL scenario, and 4) environmental impact of EOL scenario. The method is applied to a simplified model of the Power Mac G4 cube® for demonstration. [ABSTRACT FROM AUTHOR]

This paper describes two approaches that allow decomposition of structural design problems that enable structural design optimization to be performed on systems that are often seen as too large or complex to address in a single optimization. The COMPOSE method is shown to enable optimization of many tightly coupled subsystems despite the usual problem of non-convergence when a subsystem is repeatedly removed and optimized under the previously prevailing boundary conditions, then reinserted into the entire system model, which subsequently causes the boundary conditions experienced by the subsystem to change. Use of COMPOSE may allow reducing the number of whole-system evaluations necessary for component design from hundreds or thousands to fewer than ten, while still exploring the component design space extensively.Asecond approach, collaborative independent agents, is shown to address problems that have both large design spaces and time-intensive analyses, rendering them intractable to traditional methods. In an example problem, a set of loosely coupled optimization agents is shown to reduce dramatically the computing time needed to find good solutions to such problems. The savings result from the continuing transfer of results from rapid, low-refinement, less accurate search to agents that search at the full level of refinement and accuracy demanded of a solution of the problem, essentially providing them guidance as to what portions of the design space are likely to be worth searching in greater detail. [ABSTRACT FROM AUTHOR]

Deploying Evolutionary Computation (EC) solutions to real-world problems involves a wide spectrum of activities, ranging from framing the business problems and implementing the solutions to the final deployment of the solutions to the field. However, issues related to these activities are not commonly discussed in a typical EC course curriculum. Meanwhile, although the values of applied research are acknowledged by most EC technologists, the perception seems to be very narrow: success stories boost morale and high profile applications can help to secure funding for future research and can help to attract high caliber students. [ABSTRACT FROM AUTHOR]

An automated neural network-based system for predicting solar flares from their associated sunspots and simulated solar cycle is introduced. A sunspot is the cooler region of the Sun's photosphere which, thus, appears dark on the Sun's disc, and a solar flare is sudden, short lived, burst of energy on the Sun's surface, lasting from minutes to hours. The system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots and flares. Size, shape and spot density of relevant sunspots are used as input values, in addition to the values found by the solar activity model introduced by Hathaway. Two outputs are provided: The first is a flare/no flare prediction, while the second is type of the solar flare prediction (X or M type flare). Our system provides 91.7% correct prediction for the possible occurrences and, 88.3% correct prediction for the type of the solar flares. [ABSTRACT FROM AUTHOR]

This research presents an investigation into a new population inheritance approach using a concept taken from the recessive trait idea for evolutionary optimization. Evolutionary human inheritance recessive trait idea is used to enhance the effectiveness of the traditional genetic algorithms. The capability of the modified approach is explored by two examples (i) a mathematical function of two variables, and (ii) an active vibration control of a flexible beam system. Finally, a comparative performance for convergence is presented and discussed to demonstrate the merits of the modified genetic algorithms approach over the traditional ones. [ABSTRACT FROM AUTHOR]

The present paper aims to find neural network based air quality predictors, which can work with limited number of data sets and are robust enough to handle data with noise and errors. A number of available variations of neural network models such as Recurrent Network Model (RNM), Change Point detection Model with RNM (CPDM), Sequential Network Construction Model (SNCM), and Self Organizing Feature Maps (SOFM) are implemented for predicting air quality. Developed models are applied to simulate and forecast based on the long-term (annual) and short-term (daily) data. The models, in general, could predict air quality patterns with modest accuracy. However, SOFM model performed extremely well in comparison to other models for predicting long-term (annual) data as well as short-term (daily) data. [ABSTRACT FROM AUTHOR]

The majority of real optimization problems cannot be solved exactly because they have very large and highly complex search spaces. One of these complex problems is the design of looped water distribution networks, which consists of determining the best way of conveying water from the sources to the users, satisfying their requirements. This paper is to present a new memetic algorithm and evaluate its performance in this problem. With the aim to establish an accurate conclusion, other four heuristic approaches have also been adapted, including simulated annealing, mixed simulated annealing and tabu search, iterated local search, and scatter search. Results obtained in two water distribution networks demonstrate that the memetic algorithm works better when the size of the problem increases. [ABSTRACT FROM AUTHOR]

Effective pavement management requires the prioritization of the road stretches for logical disbursement of the funds available towards maintenance of the pavement. Several methods have been developed and implemented towards this goal. However, the uncertainty involved with some of the parameters has not been addressed adequately in most of the works. One such parameter has been identified as the severity of distress which is difficult to assess accurately. Hence a Fuzzy Multi Criteria Decision Making (FMCDM) approach has been proposed in this paper. For demonstration of the approach, pavement distresses with respect to their extent and severity have been collected over a number of stretches. In addition, an expert opinion survey has been carried out to quantify the influence of these parameters on the functional condition of the pavement. Priority Index (PI) has been worked out, based on which the ranking of the stretches has been arrived at. [ABSTRACT FROM AUTHOR]

A layered matrix encoding cascade genetic algorithm and particle swarm optimization approach (GA-PSO) for unit commitment and economic load dispatch problem in a thermal power system is presented in this paper. The tasks of determining and allocating power generation to different thermal units in a way that the total power production cost is at the minimum subject to equality and inequality constraints makes unit commitment and economic load dispatch challenging. A case study, based on the thermal power generation problem presented in [1], is used to demonstrate the effectiveness of the proposed method in generating a cost-effective power generation schedule. The schedule obtained is compared with that of Linear Programming (LP) as reported in [1]. The results show that the proposed GA-PSO approach outperforms LP in solving the unit commitment and economic load dispatch problem for thermal power generation system in this case study. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a competitive optimization technique for numerical optimization problems with real-parameter representation. This paper aims to investigate how DE can be adapted with binary encoding and to study its behaviors on the binary level. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a population-based and stochastic search algorithm of evolutionary computation that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. This work presents a global optimization algorithm based on DE approaches combined with local search using the implicit filtering algorithm. The implicit filtering algorithm is a projected quasi-Newton method that uses finite difference gradients. The difference increment is reduced as the optimization progresses, thereby avoiding some local minima, discontinuities, or nonsmooth regions that would trap a conventional gradient-based method. Problems involving optimization procedures of complex mathematical functions are widespread in electromagnetics. Many problems in this area can be described by nonlinear relationships, which introduce the possibility of multiple local minima. In this paper, the shape design of Loney's solenoid benchmark problem is carried out by DE approaches. The results of DE approaches are also investigated and their performance compared with those reported in the literature. [ABSTRACT FROM AUTHOR]

The work in this paper presents the use of population-based incremental learning (PBIL), one of the classic single-objective population-based optimisation methods, as a tool for multiobjective optimisation. The PBIL method with two different updating schemes of its probability vectors is presented. The performance of the two proposed multiobjective optimisers are measured and compared with four other established multiobjective evolutionary algorithms i.e. niched Pareto genetic algorithm, version 2 of non-dominated sorting genetic algorithm, version 2 of strength Pareto evolutionary algorithm, and Pareto archived evolution strategy. The optimisation methods are implemented to solve 8 bi-objective test problems where design variables are encoded as a binary string. The Pareto optimal solutions obtained from the various methods are compared and discussed. It can be concluded that, with the assigned test problems, the multiobjective PBIL methods are comparable to the previously developed algorithms in terms of convergence rate. The clear advantage in using PBILs is that they can provide considerably better population diversity. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. But research on the Quadratic Assignment Problem (QAP) has not much been investigated. In this paper, we introduce a novel approach based on PSO for QAPs. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. A new mapping is proposed between the particles in the swarm and the problem space in an efficient way. We evaluate the performance of the proposed approach with Ant Colony Optimization (ACO) algorithm. Empirical results illustrate that the approach can be applied for solving quadratic assignment problems and it has outperforms ACO in the completion time. [ABSTRACT FROM AUTHOR]

After long-term exploration, it has been well established for the mechanisms of electrocardiogram (ECG) in health monitoring of cardiovascular system. Within the frame of an intelligent home healthcare system, our research group is devoted to researching/developing various mobile health monitoring systems, including the smart ECG interpreter. Hence, in this paper, we introduce an adaptive fuzzy ECG classifier with orientation to smart ECG interpreters. It can parameterize the incoming ECG signals and then classify them into four major types for health reference: Normal (N), Premature Atria Contraction (PAC), Right Bundle Block Beat (RBBB), and Left Bundle Block Beat (LBBB). [ABSTRACT FROM AUTHOR]

This paper proposes a novel clustering algorithm for the structure learning of fuzzy neural networks. Our clustering algorithm uses the reward and penalty mechanism for the adaptation of the fuzzy neural networks prototypes at every training sample. Compared with the classical clustering algorithms, the new algorithm can on-line partition the input data, pointwise update the clusters, and self-organize the fuzzy neural structure. No priori knowledge of the input data distribution is needed for initialization. All rules are self-created, and they grow automatically with more incoming data. There are no conflicting rules in the created fuzzy neural networks. Our approach also shows that supervised clustering algorithms can be used for the structure learning of the self-organizing fuzzy neural networks. The identification of several typical nonlinear dynamic systems is developed to demonstrate the effectiveness of this learning algorithm. [ABSTRACT FROM AUTHOR]

The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed. [ABSTRACT FROM AUTHOR]

In this paper, combination between a Fuzzy ARTMAP-based artificial neural network (ANN) model and the genetic algorithm (GA) for performing cooperative learning is described. In our previous work, we have proposed a hybrid network integrating the Fuzzy ARTMAP (FAM) network with the Dynamic Decay Adjustment (DDA) algorithm (known as FAMDDA) for tackling pattern classification tasks. In this work, the FAMDDA network is employed as the platform for the GA to perform weight reinforcement. The performance of the proposed system (FAMDDA-GA) is assessed by means of generalization on unseen data from three benchmark problems. The results obtained are analyzed, discussed, and compared with those from FAM-GA. The results reveal that FAMDDA-GA performs better than FAM-GA in terms of test accuracy in the three benchmark problems. [ABSTRACT FROM AUTHOR]

We present a multi-dimensional mapping strategy using multiobjective genetic programming (MOGP) to search for the (near-)optimal feature extraction preprocessing stages for pattern classification as well as optimizing the dimensionality of the decision space. We search for the set of mappings with optimal dimensionality to project the input space into a decision space with maximized class separability. The steady-state Pareto converging genetic programming (PCGP) has been used to implement this multi-dimensional MOGP. We examine the proposed method using eight benchmark datasets from the UCI database and the Statlog project to make quantitative comparison with conventional classifiers. We conclude that MMOGP outperforms the comparator classifiers due to its optimized feature extraction process. [ABSTRACT FROM AUTHOR]

Many machine learning methods just consider the quality of prediction results as their final purpose. To make the prediction process transparent (reversible), spline kernel based methods were proposed by Gunn. However, the original solution method, termed SUpport vector Parsimonious ANOVA (SUPANOVA) was computationally very complex and demanding. In this paper, we propose a new heuristic to compute the optimal sparse vector in SUPANOVA that replaces the original solver for the convex quadratic problem of very high dimensionality. The resulting system is much faster without the loss of precision, as demonstrated in this paper on two benchmarks: the iris data set and the Boston housing market data benchmark. [ABSTRACT FROM AUTHOR]

This paper presents the results of applying a Genetic Programming (GP) based feature selection algorithm to find a small set of highly discriminating features for the detection of clinical depression from a patient's speech. While the performance of the GP-based classifiers was not as good as hoped for, several Bayesian classifiers were trained using the features found via GP and it was determined that these features do hold good discriminating power. The similarity of the feature sets found using GP for different observational groupings suggests that these features are likely to generalize well and thus provide good results with other clinical depression speech databases. [ABSTRACT FROM AUTHOR]

The design of intelligent sensor systems requires sophisticated methods from conventional signal processing and computational intelligence. Currently, a significant part of the overall system architecture still has to be manually elaborated in a tedious and time consuming process by an experienced designer. Clearly, an automatic method for auto-configuration of sensor systems would be salient. In this paper, we contribute to the optimization of the feature computation step in the overall system design, investigating multi-level thresholding (MLT) and Gaussian windowing. Our goals are to compare these two feature computation methods and two evolutionary optimization techniques, i.e., genetic algorithm (GA) and particle swarm optimization (PSO). To compare with previous research work gas sensor benchmark data is used. In the comparison of GA and PSO the latter method provided superior results of 100% recognition in generalization for thresholding, which proved to be more powerful method. [ABSTRACT FROM AUTHOR]

This paper builds on previous work in which a feature selection method based on Genetic Programming (GP) was applied to a database containing a very large set of features that were extracted from the speech of clinically depressed patients and control subjects, with the goal of finding a small set of highly discriminating features. Here, we report improved results that were obtained by applying a technique that constructs clusters of correlated features and a Genetic Algorithm (GA) search that seeks to find the set of clusters that maximizes classification accuracy. While the final feature sets are considerably larger than those previously obtained using the GP approach, the classification performance is much improved in terms of both sensitivity and specificity. The introduction of a modified fitness function that slightly favors smaller feature sets resulted in further reduction of the feature set size without any loss in classification performance. [ABSTRACT FROM AUTHOR]

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of an artificial immune network inspired optimization method — the opt-aiNet — to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods useful for building a good BSNN model for the nonlinear identification of an experimental nonlinear ball-and-tube system. [ABSTRACT FROM AUTHOR]

A design for a model-free learning adaptive control (MFLAC) based on pseudo-gradient concepts and optimization procedure by particle swarm optimization (PSO) is presented in this paper. PSO is a method for optimizing hard numerical functions on metaphor of social behavior of flocks of birds and schools of fish. A swarm consists of individuals, called particles, which change their positions over time. Each particle represents a potential solution to the problem. In a PSO system, particles fly around in a multi-dimensional search space. During its flight each particle adjusts its position according to its own experience and the experience of its neighboring particles, making use of the best position encountered by itself and its neighbors. The performance of each particle is measured according to a predefined fitness function, which is related to the problem being solved. The PSO has been found to be robust and fast in solving non-linear, non-differentiable, multi-modal problems. Motivation for application of PSO approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed. [ABSTRACT FROM AUTHOR]

B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE) — an evolutionary computation methodology — can provide a stochastic search to adjust the control points of a BSNN are proposed. DE incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution and robustness. In this paper, we propose a modified DE using chaotic sequence based on logistic map to train a BSNN. The numerical results presented here indicate that the chaotic DE is effective in building a good BSNN model for nonlinear identification of an experimental nonlinear yo-yo motion control system. [ABSTRACT FROM AUTHOR]

In our paper, the properties of the simplest Takagi-Sugeno (T-S) fuzzy controller are first investigated. Next, based on the well-known Popov criterion with graphical interpretation, a sufficient condition in the frequency domain is proposed to guarantee the globally asymptotical stability of the simplest T-S fuzzy control system. Since this sufficient condition is presented in the frequency do-main, it is of great significance in designing the simplest T-S fuzzy controller in the frequency domain. [ABSTRACT FROM AUTHOR]

Clustering is a widely applied tool of data mining to detect the hidden structure of complex multivariate datasets. Hence, clustering solves two kinds of problems simultaneously, it partitions the datasets into cluster of objects that are similar to each other and describes the clusters by cluster prototypes to provide some information about the distribution of the data. In most of the cases these cluster prototypes describe the clusters as simple geometrical objects, like spheres, ellipsoids, lines, linear subspaces etc., and the cluster prototype defines a special distance function. Unfortunately in most of the cases the user does not have prior knowledge about the number of clusters and not even about the proper shape of prototypes. The real distribution of data is generally much more complex than these simple geometrical objects, and the number of clusters depends much more on how well the chosen cluster prototypes fit the distribution of data than on the real groups within the data. This is especially true when the clusters are used for local linear modeling purposes. The aim of this paper is not to define a new distance norm based on a problem dependent cluster prototype but to show how the so called geodesic distance that is based on the exploration of the manifold the data lie on, can be used in the clustering instead of the classical Euclidean distance. The paper presents how this distance measure can be integrated within fuzzy clustering and some examples are presented to demonstrate the advantages of the proposed new methods. [ABSTRACT FROM AUTHOR]

This paper deals with the design and implementation of a software system based on Differential Evolution for the registration of images, and in its testing by means of a set of bidimensional remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A comparison is effected against a publicly available tool, showing the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, we design a sterile gesture interface for users, such as doctors/surgeons, to browse medical images in a dynamic medical environment. A vision-based gesture capture system interprets user's gestures in real-time to navigate through and manipulate an image and data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. The gesture system relies on tracking of the user's hand based on color-motion cues. A state machine switches from navigation gestures to others such as zoom and rotate. A prototype of the gesture interface was tested in an operating room by neurosurgeons conducting a live operation. Surgeon's feedback was very positive. [ABSTRACT FROM AUTHOR]

This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outlines of the objects have been used for the whole process of the recognition. Fourier Descriptors have been used as features of the objects. From the analysis and results using Fourier Descriptors, the following questions arise: What is the optimum number of descriptors to be used? Are these descriptors of equal importance? To answer these questions, the problem of selecting the best descriptors has been formulated as an optimization problem. Particle Swarm Optimization technique has been mapped and used successfully to have an object recognition system using minimal number of Fourier Descriptors. The proposed method assigns, for each of these descriptors, a weighting factor that reflects the relative importance of that descriptor. [ABSTRACT FROM AUTHOR]

The term hybrid dynamic system is a term for a mathematical system that combines behavior of a continuous nature with discontinuous changes. Such systems are often formed by the underlying computational representation of models used in the design of control and signal processing applications, for example in the automotive and aerospace industries. This paper outlines the benefits of Model-Based Design and illustrates how many different formalisms may be essential in model elaboration, such as time-based block diagrams, state transition diagrams, entity-flow networks, and multi-body diagrams. The basic elements of the underlying hybrid dynamic system computational representation are presented and it is shown how these elements combine to form different classes of behaviors that need to be handled for simulation. [ABSTRACT FROM AUTHOR]

Evolutionary computing (EC) techniques such as genetic algorithms (GA), genetic programming (GP), evolutionary programming (EP) and evolution strategies (ES) mimic nature through natural selection to perform complex optimisation processes. Grid-enabled environment provides suitable framework for EC techniques due to its computational and data capabilities. In addition, the semantic and knowledge Grids aid in the design search and exploration for multi-objective optimisation tasks. This chapter explores some problem solving environments such as Geodise (Grid-Enabled Optimisation Design Search for Engineering), FIPER (Federated Intelligent Product Environment), SOCER (Service-Oriented Concurrent Environment), DAME (Distributed Aircraft Maintenance Environment) and Globus toolkit to demonstrate how EC techniques can be performed more efficiently within a Grid environment. Service-oriented and autonomic computing features of Grid are discussed to highlight how EC algorithms can be published as services by service providers and used by service requestors dynamically. Grid computational steering and visualisation are features that can be used for real-time tuning of parameters and visual display of optimal solutions. This chapter demonstrates that grid-enabled evolutionary computing marks the future of optimisation techniques. [ABSTRACT FROM AUTHOR]

Drug discovery (DD) research has evolved to the point of critical dependence on computerized systems, databases and newer disciplines. Such disciplines include but are not limited to bioinformatics, chemoinformatics and soft computing. Their applications range from sequence analysis methods for finding biological targets to design of combinatorial libraries in lead compound optimisation. This paper presents a brief overview of classical techniques in DD with their limitations, and outlines current SC based techniques in this area. [ABSTRACT FROM AUTHOR]

Despite their horrifying destructive power, seen at Hiroshima and Nagasaki, the world has not freed itself from nuclear weapons. Nuclear weapons add a radically different dimension to the war-making capability of a nation. A nation in the modern world of economic, commercial and technological geopolitics influenced by increasing globalisation and the growing covert and overt proliferation of weapons of mass destruction (WMD) cannot remain uninfluenced by these trends and events. Applying force as a means of achieving political objectives has been followed constantly throughout the centuries by powerful nations. A major concern today when applying force to subjugate an opponent to the political will of the nation remains the question of what will happen if the other side resorts to nuclear war. This question always lingers because nuclear weapons have immense destructive power. Although it is clear that the use of nuclear weapons cannot help any nation, but rather will create many problems for it, their possession has nevertheless been advocated [2] as a deterrent to achieve political goals. [ABSTRACT FROM AUTHOR]

A RMA has been defined to take place when one of the participants in a conflict incorporates new technology, organisation and doctrine, to the extent that victory is attained in the immediate instance, but more importantly, that any other actors who might wish to deal with that participant or activity must match, or counter the new combination of technology, organisation, and doctrine in order to prevail. [ABSTRACT FROM AUTHOR]

Technology is changing the shape of warfare through multidimensional influences on the components of war-making machinery. Weapon system acquisitions by a country's armed forces require a critical evaluation of the requirements and capabilities of the alternative systems available, the adversary's equipment and the cost. Also, owing to the longer life-period of weapon systems in modern armies, changing demands on the armed forces, a dynamic world situation and the rapid pace of technological change, it becomes imperative to make decisions related to major weapon system acquisitions in an organised and comprehensive manner. Before inducting any weapon system, an army should critically evaluate the available alternatives. [ABSTRACT FROM AUTHOR]