Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

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Completely Derandomized Self-Adaptation in Evolution Strategies

I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.

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How to Solve It: Modern Heuristics

The issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation: it has a potential of adjusting the algorithm to the problem while solving the problem. In the paper we: 1) revise the terminology, which is unclear and confusing, thereby providing a classification of such control mechanisms, and 2) survey various forms of control which have been studied by the evolutionary computation community in recent years. Our classification covers the major forms of parameter control in evolutionary computation and suggests some directions for further research.

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Parameter control in evolutionary algorithms

The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters. © Springer-Verlag Berlin Heidelberg 2007.

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Parameter Control in Evolutionary Algorithms

An electronic device of an embodiment of the invention is disclosed that at least partially displays a pixel of a display image. The device includes a first reflector and a second reflector defining an optical cavity therebetween that is selective of a visible wavelength at an intensity. The device includes a mechanism to allow optical properties of the cavity to be varied such that the visible wavelength and/or the intensity are variably selectable in correspondence with the pixel of the displayable image. The device also includes one or more transparent deposited films, one or more absorbing layers, an integral micro-lens, and/or one or more anti-stiction bumps. The deposited films are over one of the reflectors, for self-packaging of the device. The absorbing layers are over one of the reflectors, to reduce undesired reflections. The integral micro-lens is over one of the reflectors, and the anti-stiction bumps are between the reflectors.

Self-packaged optical interference display device having anti-stiction bumps, integral micro-lens, and reflection-absorbing layers

An electronic device of an embodiment of the invention is for at least partially displaying a pixel of a displayable image. The electronic device includes a first reflector and a second reflector that define an optical cavity therebetween, and which is selective of a visible wavelength at an intensity by optical interference. The electronic device also includes a charge-controlling mechanism to allow optical properties of the optical cavity to be varied by controlling a predetermined amount of charge stored on the first and the second reflectors. The visible wavelength and/or the intensity are thus variably selectable in correspondence with the pixel of the displayable image.

Optical interference pixel display with charge control

An array of MEMS devices having column lines and row lines, such as a light modulator array, is controlled in response to an input signal by providing a number of discrete voltages, multiplexing from the discrete voltages a selected voltage to be applied to each MEMS device of the array, and enabling application of the selected discrete voltage to each MEMS device of the array.

Control of mems and light modulator arrays

A microfluidic device (90) for analysis of a sample The microfluidic device includes a substrate portion (94) that at least partially defines a chamber (102) for receiving the sample The substrate portion (94) includes a substrate (98) having a surface (96) The substrate portion (94) also includes a plurality of thin-film layers (110) formed on the substrate (98) adjacent the surface (96) The thin-film layers (110) form a plurality of electronic devices Each of at least two of the electronic devices is formed by a different set of the thin-film layers (110) The at least two electronic devices may include 1) a temperature control device for controlling the temperature of fluid in the chamber (102), and 2) an other electronic device configured to sense or modify a property of fluid in the chamber (102)

Microfluidic device with thin-film electronic devices

The present invention provides a data controller for controlling an electrostatically-controlled micro-electromechanical system (MEMS) device having a variable operating characteristic based on control data. The data controller comprises a data comparator and an update circuit. The data comparator is configured to receive control data of a present update cycle, to compare the control data of the present update cycle to control data of a previous update cycle on which the variable operating characteristic of the MEMS device is presently based, and to provide an update signal having a first state when the control data of the present update cycle is substantially equal to the control data of the previous update cycle. The update circuit is configured to receive the control data of the present update cycle, to receive the update signal, and to provide the control data of the present update cycle to the MEMS device, wherein the update circuit does not provide the control data of the present update cycle to the MEMS device when the update signal is in the first state.

Adam L. Ghozeil

Papers

Self-packaged optical interference display device having anti-stiction bumps, integral micro-lens, and reflection-absorbing layers

Optical interference pixel display with charge control

Control of mems and light modulator arrays

Microfluidic device with thin-film electronic devices

Selective update of micro-electromechanical device