Janne Koljonen

Solving, rating and generating Sudoku puzzles with GA

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.

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

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 and regression with good success. In all calibrations and, in particular, when optimising, it is essential to validate the model and to avoid over-fitting. GAs have a large potential when addressing these two major problems and we believe that many future applications will emerge. To conclude, optimisation gives good opportunities to simultaneously develop an accurate calibration model and to regulate model complexity and prediction ability within a considered validation framework.

Solving and analyzing Sudokus with cultural 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.

Deformation image generation for testing a strain measurement algorithm

An optical extensometer was tested using artificially deformed images with a known strain field. A real image series from a tensile test was used to obtain realistic deformation parameters, including spatial and temporal strain characteristics, changes in tonal pixel properties due to deformation, and the effect of nonuniform illumination. These parameters are used to artificially deform a real image taken from an object with a random speckle pattern. The signal-to-noise ratio of the resulting artificially deformed images is varied by applying a blurring pillbox filter and additive Gaussian noise to them. The optical extensometer uses digital image correlation to track homologous points of the object, and further to measure strains. The strain measurement algorithm includes a heuristic to dynamically control the template size in image correlation. Furthermore, several other methods to improve the accuracy-complexity ratio of the algorithm exist. The effects of different parameters and heuristics on the accuracy of the algorithm as well as its robustness against blur and noise are studied. Results show that the proposed test method is practical, and the heuristics improve the accuracy and robustness of the algorithm.

Testing the performance of a 2D nearest point algorithm with genetic algorithm generated Gaussian distributions

Genetic algorithms have successfully been used in automatic software testing. Particularly programming errors and inputs that conflict with time constraints can be found. In this paper, the idea of genetic algorithm based software testing is broadened to algorithm performance testing. It is shown how the best and worst case performance of the algorithms can be found effectively. This information can be further utilized when comparing and improving algorithms. In this paper, the proposed test method is introduced and the advantages of using genetic algorithms are discussed. Furthermore, the proposed method is applied to a 2D nearest point algorithm, which is tested by optimizing the parameters of 2D Gaussian distributions using genetic algorithms in order to find the best and worst case distributions and the corresponding performances.

Comparison of nearest point algorithms by genetic algorithms

When computational methods are developed, the efficiency of the novel methods should be compared to the existing ones. This can be done using, e.g., analytical methods and benchmark test patterns. In addition, the comparison of the best and the worst case performance is commonly of interest. In this paper, methodologies of genetic algorithm based software testing are adopted to the comparative computational testing of three varieties of dynamic two-dimensional nearest point algorithms. The extreme performances of the algorithms are searched for by optimizing the shape of two-dimensional Gaussian distributions, from which the test patterns are drawn. In particular, an approach to pairwise comparisons of computational complexities of algorithms is proposed. The test case algorithms can be sorted with respect to their computational complexity by the proposed method.

Searching strain field parameters by genetic algorithms

This paper studies the applicability of genetic algorithms and imaging to measure deformations. Genetic algorithms are used to search for the strain field parameters of images from a uniaxial tensile test. The non-deformed image is artificially deformed according to the estimated strain field parameters, and the resulting image is compared with the true deformed image. The mean difference of intensities is used as a fitness function. Results are compared with a node-based strain measurement algorithm developed by Koljonen et al. The reference method slightly outperforms the genetic algorithm as for mean difference of intensities. The root-mean-square difference of the displacement fields is less than one pixel. However, with some improvements suggested in this paper the genetic algorithm based method may be worth considering, also in other similar applications: Surface matching instead of individual landmarks can be used in camera calibration and image registration. Search of deformation parameters by genetic algorithms could be applied in pattern recognition tasks e.g. in robotics, object tracking and remote sensing if the objects are subject to deformation. In addition, other transformation parameters could be simultaneously looked for.

Near infrared wavelength relevance detection of ultraviolet radiation-induced erythema

The acute effects of sun-bathing on the near-infrared absorption spectra of human skin were studied by exposing the shoulders of a male test subject to bright Finnish high summer mid-day sun. The spectra were measured before, immediately after and for several days after exposure. Four different spectral processing and classification methods were applied to the data set to identify differences caused by exposure to the sun. The spectrophotometer and measuring procedure were found to cause some systematic errors, calling for further development, even though they could, to a large extent, be compensated for computationally. Spectral regions indicating ultraviolet radiation-induced erythema were located and the degree of erythema could be predicted correctly but the signal is weak. This paper discusses promising wavelength selection methods to study the dermal effects of exposure to the sun, as well as difficulties and remedies of near infrared spectroscopic measurements of the skin.

Dynamic template size control in digital image correlation based strain measurements

Image matching is a common procedure in computer vision. Usually the size of the image template is fixed. If the matching is done repeatedly, as e.g. in stereo vision, object tracking, and strain measurements, it is beneficial, in terms of computational cost, to use as small templates as possible. On the other hand larger templates usually give more reliable matches, unless e.g. projective distortions become too great. If the template size is controlled locally dynamically, both computational efficiency and reliability can be achieved simultaneously. Adaptive template size requires though that a larger template can be sampled anytime. This paper introduces a method to adaptively control the template size in a digital image correlation based strain measurement algorithm. The control inputs are measures of confidence of match. Some new measures are proposed in this paper, and the ones found in the literature are reviewed. The measures of confidence are tested and compared with each other as well as with a reference method using templates of fixed size. The comparison is done with respect to computational complexity and accuracy of the algorithm. Due to complex inter-actions of the free parameters of the algorithm, random search is used to find an optimal parameter combination to attain a more reliable comparison. The results show that with some confidence measures the dynamic scheme outperforms the static reference method. However, in order to benefit from the dynamic scheme, optimization of the parameters is needed.

Intersection-point determination in Virtual Keeper using machine vision

Virtual Keeper is a goalkeeper simulator. It estimates the trajectory of a ball thrown by a player using machine vision and animates a goalkeeper with a video projector. The first version of Virtual Keeper used only one camera. In this paper, a new version that uses two gray-scale cameras for trajectory estimation is proposed. In addition, a color camera and a microphone are used to determine the intersection-point of the ball trajectory and the goal line, in order to enable feedback and online calibration of the machine vision with neural networks, which in turn allows varying external parameters of the cameras and the video projector. The color camera takes images of the goal and determines the positions of the goalposts with pattern matching. After the gray-scale cameras have observed the ball and estimated its trajectory, the sound processing block is triggered. When the ball hits the screen, the noise pattern is recognized with a neural network, whose input consists of temporal and spectral features. The sound processing block in turn triggers the color camera image processing block. The color of the ball differs from the colors of the background and goalkeeper to make the segmentation problem easier. The ball is recognized with fuzzy color based segmentation and fuzzy pattern matching.