Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.

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A systematic review of search-based testing for non-functional system properties

Abstract Four optimising methods for variable selection in multivariate calibration have been described: one for determining the optimal subset of variables to give the best possible root-mean-square error of prediction (RMSEP) in a multiple linear regression (MLR) model, the second for determining the optimal subset of variables which produce a model with RMSEP less than or equal to a given value. Algorithms three and four were identical to algorithms one and two, respectively, except that this time they use a cost function derived from a partial least squares (PLS) model rather than an MLR model. Applied to a typical set of pyrolysis mass spectrometry data the first variable selection method is shown to reduce the RMSEP of the optimal MLR or PLS model significantly when the number of variables is decreased by approximately half. Alternatively, the number of variables may be reduced substantially (> 10-fold) with no loss in RMSEP.

Genetic algorithms as a method for variable selection in multiple linear regression and partial leas

The security of digital images has attracted much attention recently. In this study, a new method based on a hybrid model is proposed for image encryption. The hybrid model is composed of a genetic algorithm and a chaotic function. In the first stage of the proposed method, a number of encrypted images are constructed using the original image and the chaotic function. In the next stage, these encrypted images are used as the initial population for the genetic algorithm. In each stage of the genetic algorithm, the answer obtained from the previous iteration is optimized to produce the best-encrypted image. The best-encrypted image is defined as the image with the highest entropy and the lowest correlation coefficient among adjacent pixels. The use of genetic algorithms in image encryption has been attempted for the first time in this paper. Analyzing the results from the performed experiments, a high level of resistance of the proposed method against brute-force and statistical invasions is obviously illustrated. The obtained entropy and correlation coefficients of the method are approximately 7.9978 and −0.0009, respectively.

A hybrid genetic algorithm and chaotic function model for image encryption

Genetic algorithms (GAs) are a tool used to solve high-complexity computational problems. Apart from modelling the phenomena occurring in Nature, they help in optimization, simulation, modelling, design and prediction purposes in science, medicine, technology, and everyday life. They can be adapted to the given task, be joined with other ones (this leads to combined or hybrid methods), and can work in parallel on many processors. The uses of GAs reported in literature represent a wide variety of approaches and led to solving of numerous computational problems of high complexity. In materials science and related fields of science and technology the GAs open possibilities for materials design, studies of their properties, or production at industrial scale. Here, the recent use of GAs in various domains connected to materials science, solid state physics and chemistry, crystallography, biology, and engineering is reviewed. The listed examples taken from recent literature show how broad the use of these metho...

Genetic Algorithms, a Nature-Inspired Tool: Survey of Applications in Materials Science and Related Fields

This paper studies the problems involved in solving, rating and generating Sudoku puzzles with genetic algorithms (GA). Sudoku is a number puzzle that has recently become a worldwide phenomenon. Sudoku can be regarded as a constraint satisfaction problem. When solved with genetic algorithms it can be handled as a multi-objective optimization problem. The three objectives of this study was: 1. to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles, 2. can GA be used to generate new puzzles efficiently, and 3. can GA be used as a rating machine that evaluates the difficulty of a given Sudoku puzzle. The last of these objectives is approached by testing if puzzles that are considered difficult for a human solver are also difficult for the genetic algorithm. The results presented in this paper seem to support the conclusion that these objectives are reasonably well met with genetic algorithm optimization.

Solving, rating and generating Sudoku puzzles with GA

Global optimisation and search problems are abundant in science and engineering, including spectroscopy and its applications. Therefore, it is hardly surprising that general optimisation and search methods such as genetic algorithms (GAs) have also found applications in the area of near infrared (NIR) spectroscopy. A brief introduction to genetic algorithms, their objectives and applications in NIR spectroscopy, as well as in chemometrics, is given. The most popular application for GAs in NIR spectroscopy is wavelength, or more generally speaking, variable selection. GAs are both frequently used and convenient in multi-criteria optimisation; for example, selection of pre-processing methods, wavelength inclusion, and selection of latent variables can be optimised simultaneously. Wavelet transform has recently been applied to pre-processing of NIR data. In particular, hybrid methods of wavelets and genetic algorithms have in a number of research papers been applied to pre-processing, wavelength selection an...

A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future

This paper discusses solving and generating Sudoku puzzles with evolutionary algorithms. Sudoku is a Japanese number puzzle game that has become a worldwide phenomenon. As an optimization problem Sudoku belongs to the group of combinatorial problems, but it is also a constraint satisfaction problem. The objective of this paper is to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles and to generate new puzzles. Another goal is to find out if the puzzles, that are difficult for human solver, are also difficult for the genetic algorithms. In that case it would offer an opportunity to use genetic algorithm solver to test the difficulty levels of new Sudoku puzzles, i.e. to use it as a rating machine.

Solving and Rating Sudoku Puzzles with Genetic Algorithms

This paper studies how cultural algorithm suits to solving and analyzing Sudoku puzzles. Sudoku is a number puzzle that has recently become a worldwide phenomenon. It can be regarded as a combinatorial problem, but when solved with evolutionary algorithms it can also be handled as a constraint satisfaction or multi-objective optimization problem. The objectives of this study were 1) to test if a cultural algorithm with a belief space solves Sudoku puzzles more efficiently than a normal permutation genetic algorithm, 2) to see if the belief space gathers information that helps analyze the results and improve the method accordingly, 3) to improve our previous Sudoku solver presented in CEC2007. Experiments showed that proposed the cultural algorithm performed slightly better than the previous genetic algorithm based Sudoku solver.

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Solving and analyzing Sudokus with cultural algorithms

Logistics is an important driver for the competitiveness of industries and material supply. The development of smart logistics, powered by precise positioning and communication technologies can significantly improve the efficiency of logistics. The emerging technology of ultra-wideband (UWB) precision positioning has attracted significant attention throughout the previous decade owing to its promising capabilities over other radio frequency-based indoor localisation systems. In addition, UWB is characterised by large bandwidth and data rate, short message length, low transmission power and high penetration capability, which are all favourable for indoor positioning applications. However, UWB localisation technology faces several challenges that are somewhat similar to other technologies, such as mitigating errors that originate from non-line-of-sight (NLOS) situations and tackling signal interference in dense environments, and when required to operate in extreme conditions. This paper reviews the most recent advances made in UWB positioning systems over the last five years, with a focus on high-ranking articles. In addition to going through more conventional solutions to UWB challenges, modern solutions, which involve the use of machine learning and sensor data fusion, are discussed. We highlight the most promising findings of the recently implemented and foreseen UWB positioning systems by providing a summary of each reviewed article. Additionally, we address a major challenge that faces the UWB positioning technology: NLOS situations, focusing on some proposed remedies such as multi-sensor fusion and machine learning. As an application, this study introduces how UWB technology promotes smart logistics by offering indoor positioning to improve efficiencies in the delivery of goods from the source to the customer. Furthermore, it demonstrates the benefits of UWB technology for accurate positioning and tracking of both stationary and moving items, and machinery in an indoor logistics environment.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09761257.pdf

Janne Koljonen

Papers

Solving, rating and generating Sudoku puzzles with GA

A review of genetic algorithms in near infrared spectroscopy and chemometrics: past and future

Solving and Rating Sudoku Puzzles with Genetic Algorithms

Solving and analyzing Sudokus with cultural algorithms

Precision Positioning for Smart Logistics Using Ultra-Wideband Technology-Based Indoor Navigation: A Review