Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

/pdf/evolutionary-computation-comments-on-the-history-and-current-2wv5xjjqp7.pdf

Evolutionary computation: comments on the history and current state

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.

Evolutionary programming techniques for economic load dispatch

The purpose of this paper is to give an overview of the recent surgical intraoperational applications of indocyanine green fluorescence imaging methods, the basics of the technology, and instrumentation used. Well over 200 papers describing this technique in clinical setting are reviewed. In addition to the surgical applications, other recent medical applications of ICG are briefly examined.

/pdf/a-review-of-indocyanine-green-fluorescent-imaging-in-surgery-4z5ze14r4u.pdf

A review of indocyanine green fluorescent imaging in surgery

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A description is given of the results of experiments to find the optimum population size for genetic algorithms as a function of problem complexity. It seems that for moderate problem complexity the optimal population size for problems coded as bitstrings is approximately the length of the string in bits for sequential machines. This result is also consistent with earlier experimentation. In parallel architectures the optimal population size is larger than in the corresponding sequential cases, but the exact figures seem to be sensitive to implementation details. >

On optimal population size of genetic algorithms

Please nd enclosed the result of my GA bibliography database query. For further information see 1]. I would be happy if you could send me other references that you perhaps manage to nd on the subject. It would be also a pleasure to include your contributions to the bibliography.

An Indexed Bibliography of Genetic Algorithms

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

/pdf/a-review-of-optical-nondestructive-visual-and-near-infrared-3rju5tqou5.pdf

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

In this paper, we review the work applying computational evolutionary methods in software engineering, especially in software testing. Testing is both technically and economically vital for high quality software production. About half of the expenses in software production has been estimated to be due to testing. Much of the testing is done manually or using other labor-intensive methods. To develop efficient, cost effective, and automatic means and tools for software testing is thus highly tempting for software industry. Searching software errors by using evolution based methods like genetic algorithms is one attempt towards these goals. Software testing is a field, where the gap between the means and needs is exceptionally wide. Despite the great advances in computing during the last 30 years the software development and the testing process in most companies is still very immature, meanwhile the complexity and criticality of the software has increased tremendously. When testing software, by using any optimization method as a test data generator, we are optimizing the given input according to a selected software metric encoded as a fitness function. The success of genetic algorithms in optimization is based on the so called building block hypothesis. Basically, the genetic algorithms do not find any solitary bug at any higher probability than pure random search. However, evolutionary algorithms adapt to the given problem, in practice this means that a genetic algorithm-based tester generates several parameter combinations that reveal minor bugs and based on this information constructs sequences that will reveal, on the average, more bugs than pure random testing.

Jarmo T. Alander

Papers

A review of indocyanine green fluorescent imaging in surgery

On optimal population size of genetic algorithms

An Indexed Bibliography of Genetic Algorithms

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Evolutionary software engineering, a review