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The purpose of this paper is to study the coupled fixed point problem and the coupled best proximity problem for single-valued and multi-valued contraction type operators defined on cyclic representations of the space. The approach is based on fixed point results for appropriate operators generated by the initial problems.

The purpose of this paper is to provide an improved genetic algorithm to solve 0/1 multidimensional knapsack problem (0/1 MKP), by proposing new selection and crossover operators that cooperate to explore the search space.

The authors first present a new sexual selection strategy that significantly improves the one proposed by (Varnamkhasti and Lee, 2012), while working in phenotype space. Then they propose two variants of the two-stage recombination operator of (Aghezzaf and Naimi, 2009), while they adapt the latter in the context of 0/1 MKP. The authors evaluate the efficiency of both proposed operators on a large set of 0/1 MKP benchmark instances. The obtained results are compared against that of conventional selection and crossover operators, in terms of solution quality and computing time.

The paper shows that the proposed selection respects the two major factors of any metaheuristic: exploration and exploitation aspects. Furthermore, the first variant of the two-stage recombination operator pushes the search space towards exploitation, while the second variant increases the genetic diversity. The paper then demonstrates that the improved genetic algorithm combining the two proposed operators is a competitive method for solving the 0/1 MKP.

Although only 0/1 MKP standard instances were tested in the empirical experiments in this paper, the improved genetic algorithm can be used as a powerful tool to solve many real-world applications of 0/1 MKP, as the latter models several industrial and investment issues. Moreover, the proposed selection and crossover operators can be incorporated into other bio-inspired algorithms to improve their performance. Furthermore, the two proposed operators can be adapted to solve other binary combinatorial optimization problems.

This research study provides an effective solution for a well-known non-deterministic polynomial-time (NP)-hard combinatorial optimization problem; that is 0/1 MKP, by tackling it with an improved genetic algorithm. The proposed evolutionary mechanism is based on two new genetic operators. The first proposed operator is a new and deeply different variant of the so-called sexual selection that has been rarely addressed in the literature. The second proposed operator is an adaptation of the two-stage recombination operator in the 0/1 MKP context. This adaptation results in two variants of the two-stage recombination operator that aim to improve the quality of encountered solutions, while taking advantage of the sexual selection criteria to prevent the classical issue of genetic algorithm that is premature convergence.

This study aims to show how laser scanning data can be utilised to quantitatively assess “blind lifts” with respect to their rate and spatial distribution.

This study employed time study of crane cycles for quantitative measuring of the crane’s work periods in dead areas and mapping the crane operator’s field of view and developing a model that allows the spatial analysis of blind lifts.

This study found a discrete geometric laser scan-based model that is capable of locating and quantifying the visible and invisible zones from the crane operator’s cabin; 28 per cent of the analyzed crane’s work area represented by the model were found to be invisible, which corresponds fairly to 35 per cent of the half-cycles measured manually that were found to involve blind lifting; the range of blind lifts duration derived from the spatial information-based model was 50 to 84 per cent, which is in excellent correspondence with the 54 per cent to 82 per cent range obtained from the time unit-based analysis.

The laser-based model and the ensuing analyses are limited to the type of buildings whose envelope can practically be represented by the vertical extrusion of their footprint.

The practical implications of the study are reduction of blind lifts as a factor when selecting the location of the crane and staging areas; more effective preplanning of signallers positioning; and ad hoc consideration of analysed dead space for various lift task-based decision-making during construction.

This study demonstrates the ability to capture the geometric relations that characterise the work scene around the tower crane by harnessing the increasingly available laser technology and correlates the results of the manual observations with those obtained from the laser-based model.

This paper seeks to study how interorganizational governance mechanisms within mobile eco‐systems are affected by the end of the walled gardens and what this implies for developing mobile internet services.

Starting from concepts on interorganizational governance, the paper conducts an extensive case study on how the Dutch walled garden i‐mode portal evolved in an open WAP‐based portal.

The transition of walled garden to open portals dramatically changes governance mechanisms between operator and content providers. Authority‐based governance in the form of operator rules, contracts‐based governance in the form of formalized agreements, and trust‐based governance in the form of close collaboration all reduced following the end of the walled garden.

The author demonstrates that theoretical concepts of interorganizational governance are relevant for actors within the mobile ecosystem to understand, next to regulatory, technical and market mechanisms, if they are to provide value to the customers as well as to the eco‐system itself.

Although scholars often agree that the choice between walled gardens and open models will influence service innovation, existing studies do not systematically study how governance between operators and content providers changes when the mobile eco‐system is transforming from walled gardens to open models. Although this paper focuses on the relation between operators and content providers, the power shift to hardware and platform providers implies that governance is still highly relevant. As walled gardens also emerge in other areas of ICT‐enabled services, for instance in the Smart Living domain, the insights will be valuable for studies on ICT‐enabled service industries as well.

This paper provides an introduction to genetic algorithms, a new approach to the investigation of computationally‐intensive problems that may be insoluble using conventional, deterministic approaches. A genetic algorithm takes an initial set of possible starting solutions and then iteratively improves these solutions using operators that are analogous to those involved in Darwinian evolution. The approach is illustrated by reference to several problems in information retrieval.

Examines work on the development of an undersea remotely operated vehicle which is both unmanned and untethered. Outlines the difficulty of the only suitable long‐range underwater communication system being an acoustic link which has a very low bandwidth and a very large delay. The teleprogramming system seeks to overcome this difficulty by creating a computer simulation of the remote environment and allowing the operator to perform his task entirely within that simulated world. Describes the operator station, command generation to the ROV, the detection of errors and the hardware implementation. Concludes that the teleprogramming systems provides a means of performing tasks efficiently, even when the communication between operator and remote site occurs via a low bandwidth, high delay, communications link.

This study aims to construct a Weber point-based model to complete the visualization of preference aggregation in group decision-making problem, in which decision-makers are associated with trust relationship.

This study mainly comprises four parts: trust propagation, preference aggregation, opinion adjustment and alternative selection. Firstly, the incomplete trust between decision-makers is completed with trust transfer operators and propagation probability in trust propagation process. Secondly, a preference aggregation model based on Weber point is proposed to aggregate the group preference visually. Thirdly, opinions are adjusted to reach a consensus. Finally, the ranking of alternatives is determined by the correlation coefficient with the group preference as a reference.

The Weber point-based model proposed in this study can minimize the gap in the preference of alternatives between the group and all decision-makers, and realize the visualization of aggregation result. A case of plan selection is introduced to illustrate the feasibility and effectiveness of the proposed model.

By comparing the result with the weighted average-based preference aggregation method, the Weber point-based model proposed in this study can show the result of preference aggregation intuitively and improve group consensus.

This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus (FRC) and genetic algorithm (GA).

The paper introduces a new interpretation of multidimensional fuzzy implication (MFI) to represent the author's knowledge about the training data set. It also considers the notion of a fuzzy pattern vector (FPV) to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one‐dimensional fuzzy implication. For the estimation of Ri floating point representation of GA is used. Thus, a set of fuzzy relations is formed from the new interpretation of MFI. This set of fuzzy relations is termed as the core of the pattern classifier. Once the classifier is constructed the non‐fuzzy features of a test pattern can be classified.

The performance of the proposed scheme is tested on synthetic data. Subsequently, the paper uses the proposed scheme for the vowel classification problem of an Indian language. In all these case studies the recognition score of the proposed method is very good. Finally, a benchmark of performance is established by considering Multilayer Perceptron (MLP), Support Vector Machine (SVM) and the proposed method. The Abalone, Hosse colic and Pima Indians data sets, obtained from UCL database repository are used for the said benchmark study. The benchmark study also establishes the superiority of the proposed method.

This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV. A set of fuzzy relations which is the core of the pattern classifier, is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed. This new approach to pattern classification avoids the curse of high dimensionality of feature vector. It can provide multiple classifications under overlapped classes.

Advancements in printed wiring technology have led to an increased demand for technicians with the knowledge and manual skills necessary to perform modifications, rework or repairs to these assemblies. Five key requirements are necessary to control the process for repair. These elements include technical expertise, analytical assessment, structured procedures, proper tooling and performance inspection criteria. In this paper all five elements are analysed and selected guidelines are given for common rework and repair situations.

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.