The selection monad has proven useful for modelling exhaustive search algorithms. It is well studied in the area of game theory as an elegant way of expressing algorithms that calculate optimal plays for sequential ga...
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ISBN:
(纸本)9783031213137;9783031213144
The selection monad has proven useful for modelling exhaustive search algorithms. It is well studied in the area of game theory as an elegant way of expressing algorithms that calculate optimal plays for sequential games with perfect information;composition of moves is modeled as a `product' of selection functions. This paper aims to expand the application of the selection monad to other classes of algorithms. The structure used to describe exhaustive search problems can easily be applied to greedy algorithms;with some changes to the product function, the behaviour of the selection monad can be changed from an exhaustive search behaviour to a greedy one. This enables an algorithm design framework in which the behaviour of the algorithm can be exchanged modularly by using different product functions.
algorithms are the essence of computational thinking, which refers to a set of problem-solving processes that help children become logical thinkers in this increasingly digital society. It is important for teachers of...
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algorithms are the essence of computational thinking, which refers to a set of problem-solving processes that help children become logical thinkers in this increasingly digital society. It is important for teachers of young children to carefully plan and implement algorithm design tasks that involve repeated step-by-step procedures to build strong foundational computational thinking skills. In this article, the authors present algorithm tasks, including following a recipe, creating a treasure map, modeling how to perform a task, and sharing a routine, which can be easily integrated in the daily activities in early childhood classrooms. Fostering young children's aptitude for algorithm-specific thinking-and-doing processes creates a foundation for logical thinking.
This paper introduces an architecture-independent, hierarchical approach to algorithm design on distributed-memory architectures, in contrast to the current trend of tailoring algorithms towards specific architectures...
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This paper introduces an architecture-independent, hierarchical approach to algorithm design on distributed-memory architectures, in contrast to the current trend of tailoring algorithms towards specific architectures. We show that, rather surprisingly, this new approach can achieve uniformity without sacrificing efficiency. In our framework, there are three levels of algorithm design: design of a network-independent algorithm in a network-independent programming environment, design of virtual networks (virtual architectures) for the algorithm, and design of emulations of the virtual networks on physical networks. In its organizational principle, this methodology is analogous to the abstract data structure approach to sequential algorithm design. We propose the following thesis: architecture-independent optimality can lead to portable optimality. Namely, a single network-independent algorithm, when optimized network-independently, with the support of properly chosen virtual networks, can be implemented on a wide spectrum of physical networks to achieve optimality on each of them with respect to both computation and communication, We illustrate this thesis with an analysis of the example of algorithm design for ordinary matrix multiplication. In a paper by Gao, a general theory of portable optimality of parallel algorithms is presented. Besides its implications to the methodology of parallel algorithm design, our framework also suggests new questions for theoretical research in parallel computation on interconnection networks. (C) 1996 Academic Press, Inc.
Using salt caverns and caves to build compressed air energy storage power stations is an important development direction in the field of large-scale energy storage. With the continuous maturity of technology, compress...
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Using salt caverns and caves to build compressed air energy storage power stations is an important development direction in the field of large-scale energy storage. With the continuous maturity of technology, compressed air energy storage generators have gradually developed into large-scale units with a single-unit capacity of 300 MW. Due to the increase of single-unit capacity, the internal short-circuit fault of the stator will cause very serious losses. In addition, the compressed air energy storage generator has complex operating conditions and is more prone to various internal short-circuit faults. In order to effectively ensure its safe and reliable operation, it is particularly necessary to study the effective main protection configuration scheme. The optimal design of the main protection configuration scheme is based on the analysis of the possible internal short-circuit faults of the generator. At present, the stator windings of large-scale compressed air energy storage generators generally adopt double-layer wave winding or double-layer tap winding structures. For these two winding structures, this paper analyzes in detail the characteristics of all fault modalities that may occur inside the large-capacity compressed air energy storage generator and designs a general algorithm that can form their respective fault sets. Finally, the application of the algorithm in the analysis of the stator fault modality in a compressed air energy storage power station is introduced. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
This article focuses on the key influencing factors and prediction accuracy of diabetes. Nine test indexes were mainly considered: low density lipoprotein, triglyceride, total cholesterol, white blood cell, temperatur...
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This article focuses on the key influencing factors and prediction accuracy of diabetes. Nine test indexes were mainly considered: low density lipoprotein, triglyceride, total cholesterol, white blood cell, temperature, blood pressure, heart rate, blood sugar, and age. By designing data experiment method and logistic regression prediction algorithm based on fivefold cross validation, it is used to analyze odds ratio, full subset screening regression, cross validation, root mean square error and confusion matrix on 96 original data samples with 76 diabetes patients and 20 non-diabetes patients. Based on statistical test and innovation, two modeling ideas based on the combination of clinical experience and statistical test are proposed. Logistic regression model I with 2 parameter variables and Logistic regression model II with 5 parameter variables are, respectively, established to predict and compare the accuracy of five groups of different cross validation test sets. The prediction accuracy of the former is 93.7895%, and that of the latter is 91.7895%. This study found that age and blood sugar are the key influencing factors of diabetes. However, total cholesterol, temperature and white blood cell have little effect on diabetes. The research method has high application value and can provide scientific solutions for medical institutions to predict, analyze and early diagnose diabetes.
The paper presents a novel approach to formal algorithm design for a typical class of discrete optimization problems. Using a concise set of program calculation rules, our approach reduces a problem into subproblems w...
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The paper presents a novel approach to formal algorithm design for a typical class of discrete optimization problems. Using a concise set of program calculation rules, our approach reduces a problem into subproblems with less complexity based on function decompositions, constructs the problem reduction graph that describes the recurrence relations between the problem and subproblems, from which a provably correct algorithm can be mechanically derived. Our approach covers a large variety of algorithms and bridges the relationship between conventional methods for designing efficient algorithms (including dynamic programming and greedy) and some effective methods for coping with intractability (including approximation and parameterization).
designing an algorithm to solve a given problem is a challenging task due to the variety of possible design choices and the lack of clear guidelines on how to choose and/or combine them. Optimization and machine learn...
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designing an algorithm to solve a given problem is a challenging task due to the variety of possible design choices and the lack of clear guidelines on how to choose and/or combine them. Optimization and machine learning techniques have been used to make the algorithm design process more independent on human intervention. Hyper-heuristic approaches, in particular, have been proposed to search the space of algorithms/heuristics and/or their components, and iteratively combine and adapt them for specific problems. Although flexible to produce customized algorithms, hyper-heuristics can be extremely costly procedures. This paper proposes a novel hybrid hyper-heuristic (H3AD), which combines an automated algorithm selection approach with a generative hyper-heuristic. This combination intends to reduce the cost of providing an algorithm for a new input problem by reusing algorithms previously built by hyper-heuristics to solve similar problems. H3AD was evaluated in a case study to optimize the design of Particle Swarm Optimization algorithms in unconstrained continuous optimization problems. The results showed that H3AD provided appropriate recommendations of algorithms, reusing the algorithms generated by the hyper-heuristic to new input problems. Besides, H3AD drastically reduced the time of providing a customized algorithm when compared to generative hyper-heuristics, without a significant loss of optimization performance. (C) 2017 Elsevier Inc. All rights reserved.
The design of a missile guidance algorithm valid for any initial missile and target orientation is described. The limitations of proportional navigation- (PN-) or line-of-sight rate-based guidance for this problem are...
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The design of a missile guidance algorithm valid for any initial missile and target orientation is described. The limitations of proportional navigation- (PN-) or line-of-sight rate-based guidance for this problem are pointed out. design heuristics are explained, and general guidelines for the synthesis of guidance algorithms are stated. It is shown that the design of an algorithm valid for any initial missile and target orientation leads to a nonlinear inverse problem. Standard geometric methods for solving nonlinear inverse problems are adapted to derive a new class of algorithms based on the relative heading error angle (RHEA). Examples of RHEA algorithms are then discussed and compared with PN guidance for different engagement scenarios.
Recently we have discovered an error in the implementation of the mutation operator in our earlier work on robust evolutionary algorithm design for socio-economic simulation (Alkemade et al. 2006, 2007). The original ...
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Recently we have discovered an error in the implementation of the mutation operator in our earlier work on robust evolutionary algorithm design for socio-economic simulation (Alkemade et al. 2006, 2007). The original paper compared two commonly used approaches to socio-economic simulation. In the first approach parameter settings for the evolutionary algorithm are directly derived from the underlying economic model while in the second approach to social learning parameter settings are chosen so as to optimise evolutionary algorithm performance. Main conclusions of the original paper are that the first approach may hinder the performance of the evolutionary algorithm and thereby hinder agent learning, that is, that social learning evolutionary algorithms are able to overcome the so-called spite-effect and obtain high profit outcomes. These main conclusions are still confirmed when the error in the mutation operator is corrected. However, the convergence behaviour of some of the individual runs differs significantly from the (incorrect) results presented in the earlier papers. More specifically, in the corrected experiments we do not observe the same type of premature convergence in approach I. In this paper we present the corrected results.
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