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.

all rights reserved Genetic algorithms (Gas) have been successfully applied to many difficult search and optimisation problems in a diversity of research domains, including chemometrics and near infrared (nIr) spectroscopy. the application of Gas in chemometrics has previously been reviewed by riccardo leardi. 1,2 Ga applications in regression problems of chemometrics, molecular modelling, and various other applications related to chemistry are discussed in the first review. 1 the second review 2 is mainly a general introduction to Gas with chemistryrelated examples, but a few applications are also reviewed. leardi has also written a book on Gas and artificial neural networks (anns) in chemometrics. 3 Several fields related to chemometrics and chemistry, such as molecular modelling, design and recognition 4,5 and computation of protein folding, 6 have also benefited from tutorials and reviews of Gas. this review focuses on applications that include both Gas in methodology and spectroscopic data recorded in the nearinfrared range (Ga-nIr). In addition, some closely related areas are also partly covered. the section on variable and wavelength selection methods partly overlaps with leardi's review. 1 the emphasis is on: (a) how Gas have been performing relative to other optimisation methods, (b) the problem of over-fitting and (c) multi-criteria optimisation. the third section deals with multi-criteria optimisation and optimised pre-processing

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.

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