CAPS is a highly interactive diagnostic compiler/interpreter that allows beginning programmers to prepare, debug, and execute fairly simple programs at a graphics display terminal. Complete syntax checking and most se...
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programming a robot takes time, effort, and expert knowledge. As robots find their way to our personal spaces, it becomes urgent to investigate more intuitive methods to program them. An emerging field of research has...
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This paper considers interactive decision making methods for random fuzzy two-level linear programming problems. Assuming that the decision makers concern about the probabilities that their own objective function valu...
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This paper considers interactive decision making methods for random fuzzy two-level linear programming problems. Assuming that the decision makers concern about the probabilities that their own objective function values are smaller than or equal to certain target values, fuzzy goals of the decision makers for the probabilities are introduced. Then, the possibility-based probability model to maximize the degrees of possibility with respect to the attained probability is considered. interactive fuzzy nonlinear programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method. (C) 2013 Elsevier Inc. All rights reserved.
Two major approaches to deal with randomness or ambiguity involved in mathematical programming problems have been developed. They are stochastic programming approaches and fuzzy programming approaches. In this paper, ...
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Two major approaches to deal with randomness or ambiguity involved in mathematical programming problems have been developed. They are stochastic programming approaches and fuzzy programming approaches. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. Using the probability maximization model to maximize the probability that each objective function becomes a certain value under chance constrained conditions, the stochastic programming problems are transformed into deterministic ones. As a fusion of stochastic approaches and fuzzy ones, after determining the fuzzy, goals of the decision maker, an interactive fuzzy satisficing method to derive a satisficing solution for the decision maker by updating the reference membership levels is presented. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method. (C) 2004 Elsevier B.V. All rights reserved.
For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been ...
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For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various,ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper, focusing on general multiobjective 0-1 programming problems involving positive and negative coefficients, we propose an interactive fuzzy satisficing method by extending our previous genetic algorithms ...
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In this paper, focusing on general multiobjective 0-1 programming problems involving positive and negative coefficients, we propose an interactive fuzzy satisficing method by extending our previous genetic algorithms with double strings for multiobjective multidimensional 0-1 knapsack problems. In the extended genetic algorithms, a new decoding algorithm for individuals represented by double strings which maps each individual to a feasible solution is proposed through the introduction of backtracking and individual modification. After examining the feasibility and efficiency of the extended genetic algorithms with double strings using a lot of 0-1 programming problems involving positive and negative coefficients, an illustrative numerical example demonstrated the feasibility and efficiency of the proposed interactive fuzzy satisficing method. (C) 2002 Elsevier Science B.V. All rights reserved.
In the past two decades, there has been a significant increase in the number of interactive algorithms proposed for solving multiple objective mathematical programming (MOMP) problems. Most of these procedures have ne...
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In the past two decades, there has been a significant increase in the number of interactive algorithms proposed for solving multiple objective mathematical programming (MOMP) problems. Most of these procedures have neither been tested in real decision making situations, nor compared to each other. In this study, we emphasize the importance of comparative studies of interactive MOMP procedures and present a state of the art review. Our scope is limited to the comparisons of interactive procedures for solving deterministic, linear, integer or nonlinear constrained multiple objective optimization problems involving a single decision maker.
In this era of smart devices, new technologies, gadgets, apps, and numerous systems and services available over online, teaching an introductory programming course by traditional lecture method faces challenges to dra...
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ISBN:
(纸本)9781450367936
In this era of smart devices, new technologies, gadgets, apps, and numerous systems and services available over online, teaching an introductory programming course by traditional lecture method faces challenges to draw student's attention; especially in their freshman year. In this work, we discuss our experience in teaching an introductory CS course by infusing both interactive and collaborative learning in pedagogy so that students can learn using interactive platforms, tools, technologies, systems, and services as available to them and collaboration within and among groups. For interactive learning, students used an interactive programming environment (e.g. *** classroom) as well as online eBooks. We designed several in-class exercises, assignments, small lab-based projects with example codes and expected outputs, and unit tests by using built-in unit tests library. We also, in the middle of semester, introduced collaborative learning through teamwork on well-defined projects during the learning time and submitted at the end. The collaborations include use of basic task management tools and multi-player tool of *** that the students can critic, supplement, improve peer works and learn. To evaluate the impact of this infusion, a pre- and post-survey were conducted on student cohort in two different semesters. The initial evaluation of the survey results and performances (final project and final grades) show evidence to conclude that the proposed pedagogical approach increased student motivation and engagement and facilitated learning to entry-level computer science students.
Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven to...
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ISBN:
(纸本)9798350364613;9798350364606
Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.
In this paper, an interactive fuzzy satisficing method for a multiobjective 0-1 programming problem is presented by incorporating the desirable features of both the interactive fuzzy programming methods and genetic al...
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In this paper, an interactive fuzzy satisficing method for a multiobjective 0-1 programming problem is presented by incorporating the desirable features of both the interactive fuzzy programming methods and genetic algorithms. By considering the imprecise nature of human judgements, the fuzzy goals of the decision maker (DM) for objective functions are quantified by eliciting linear membership functions. If the DM specifies the reference membership levels for all of the membership functions, the corresponding Pareto optimal solution which is, in the minimax sense, nearest to the requirement can be obtained by solving the minimax problem. To generate Pareto optimal solutions by applying the proposed genetic algorithm which is modified to generate only feasible solutions, the algorithm is further revised by providing some new mechanism for forming an initial population after the first interaction with the DM. illustrative numerical examples demonstrate the both feasibility and efficiency of the proposed methods.
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