Journal articles: 'Original programming'

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Mweshi, George. "Feature Selection using Genetic Programming." Zambia ICT Journal 3, no. 2 (2019): 11–18. http://dx.doi.org/10.33260/zictjournal.v3i2.62.

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Extracting useful and novel information from the large amount of collected data has become a necessity for corporations wishing to maintain a competitive advantage. One of the biggest issues in handling these significantly large datasets is the curse of dimensionality. As the dimension of the data increases, the performance of the data mining algorithms employed to mine the data deteriorates. This deterioration is mainly caused by the large search space created as a result of having irrelevant, noisy and redundant features in the data. Feature selection is one of the various techniques that can be used to remove these unnecessary features. Feature selection consequently reduces the dimension of the data as well as the search space which in turn increases the efficiency and the accuracy of the mining algorithms. In this paper, we investigate the ability of Genetic Programming (GP), an evolutionary algorithm searching strategy capable of automatically finding solutions in complex and large search spaces, to perform feature selection. We implement a basic GP algorithm and perform feature selection on 5 benchmark classification datasets from UCI repository. To test the competitiveness and feasibility of the GP approach, we examine the classification performance of four classifiers namely J48, Naives Bayes, PART, and Random Forests using the GP selected features, all the original features and the features selected by the other commonly used feature selection techniques i.e. principal component analysis, information gain, relief-f and cfs. The experimental results show that not only does GP select a smaller set of features from the original features, classifiers using GP selected features achieve a better classification performance than using all the original features. Furthermore, compared to the other well-known feature selection techniques, GP achieves very competitive results.

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Antczak, Tadeusz. "Saddle point criteria and duality in multiobjective programming via an η-approximation method". ANZIAM Journal 47, № 2 (2005): 155–72. http://dx.doi.org/10.1017/s1446181100009962.

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AbstractIn this paper, Antczak's η-approximation approach is used to prove the equivalence between optima of multiobjective programming problems and the η-saddle points of the associated η-approximated vector optimisation problems. We introduce an η-Lagrange function for a constructed η-approximated vector optimisation problem and present some modified η-saddle point results. Furthermore, we construct an η-approximated Mond-Weir dual problem associated with the original dual problem of the considered multiobjective programming problem. Using duality theorems between η-approximation vector optimisation problems and their duals (that is, an η-approximated dual problem), various duality theorems are established for the original multiobjective programming problem and its original Mond-Weir dual problem.

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Hasuike, Takashi, Hideki Katagiri, and Hiroaki Ishii. "Multiobjective Random Fuzzy Linear Programming Problems Based on the Possibility Maximization Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 373–79. http://dx.doi.org/10.20965/jaciii.2009.p0373.

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Two multiobjective random fuzzy programming problems considered based on the possibility maximization model using possibilistic and stochastic programming are not initially well defined due to the random variables and fuzzy numbers included. To solve them analytically, probability criteria are set for objective functions and chance constraints are introduced. Taking into account the decision maker’s subjectivity and the original plan’s flexibility, a fuzzy goal is introduced for each objective function. The original problems are then changed into deterministic equivalent problems to make the possibility fractile optimization problem equivalent to a linear programming problem. The possibility maximization problem for probability is changed into a nonlinear programming problem, and an analytical solution is constructed extending previous solution approaches.

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Zhang, Chengxue, Debin Kong, Peng Pan, and Mingyuan Zhou. "A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs." Journal of Mathematics 2021 (February 10, 2021): 1–4. http://dx.doi.org/10.1155/2021/6651480.

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In a linear programming for horizontally partitioned data, the equality constraint matrix is divided into groups of rows. Each group of the matrix rows and the corresponding right-hand side vector are owned by different entities, and these entities are reluctant to disclose their own groups of rows or right-hand side vectors. To calculate the optimal solution for the linear programming in this case, Mangasarian used a random matrix of full rank with probability 1, but an event with probability 1 is not a certain event, so a random matrix of full rank with probability 1 does not certainly happen. In this way, the solution of the original linear programming is not equal to the solution of the secure linear programming. We used an invertible random matrix for this shortcoming. The invertible random matrix converted the original linear programming problem to a secure linear program problem. This secure linear programming will not reveal any of the privately held data.

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Lin, Hongwei, Yuping Wang, and Xiaoli Wang. "An Auxiliary Function Method for Global Minimization in Integer Programming." Mathematical Problems in Engineering 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/402437.

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An auxiliary function method is proposed for finding the global minimizer of integer programming problem. Firstly, we propose a method to transform the original problem into an integer programming with box constraint, which does not change the properties of the original problem. For the transformed problem, we propose an auxiliary function to escape from the current local minimizer and to get a better one. Then, based on the proposed auxiliary function, a new algorithm to find the global minimizer of integer programming is proposed. At last, numerical results are given to demonstrate the effectiveness and efficiency of the proposed method.

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Feng, Qi Gao, Han Ping Mao, Yan Fang Wei, and Hong Wei Jiao. "New Computational Method for a Class of Optimization Problems in Production System and System Engineering." Applied Mechanics and Materials 29-32 (August 2010): 1016–21. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.1016.

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In this paper, a new computational method is proposed for solving a class of optimization problems which have broad applications in production system and system engineering. Firstly, by exploiting structure of the problem, linear relaxation programming of the original problem is constructed. By using simplex method we can solve a sequence of linear relaxation programming, the proposed algorithm is convergent to the global minimum of original problem through the successive refinement of the feasible region of a series of linear programming problems. In finally, numerical experiments are given to show the feasible of the proposed method.

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Al-Bayati, Abbas Y., and Huda E. Khalid. "An Original Geometric Programming Problem Algorithm to Solve Two Coefficients Sensitivity Analysis." American Journal of Engineering and Applied Sciences 2, no. 2 (2009): 481–87. http://dx.doi.org/10.3844/ajeassp.2009.481.487.

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ANTCZAK, TADEUSZ. "AN η-APPROXIMATION APPROACH IN NONLINEAR VECTOR OPTIMIZATION WITH UNIVEX FUNCTIONS". Asia-Pacific Journal of Operational Research 23, № 04 (2006): 525–42. http://dx.doi.org/10.1142/s0217595906001029.

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In this paper, the so-called η-approximation approach is used to obtain the sufficient conditions for a nonlinear multiobjective programming problem with univex functions with respect to the same function η. In this method, an equivalent η-approximated vector optimization problem is constructed by a modification of both the objective and the constraint functions in the original multiobjective programming problem at the given feasible point. Moreover, to find the optimal solutions of the original multiobjective problem, it sufficies to solve its associated η-approximated vector optimization problem. Finally, the description of the η-approximation algorithm for solving a nonlinear multiobjective programming problem involving univex functions is presented.

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Gabriele, G. A., and T. J. Beltracchi. "An Investigation of Pshenichnyi’s Recursive Quadratic Programming Method for Engineering Optimization." Journal of Mechanisms, Transmissions, and Automation in Design 109, no. 2 (1987): 248–53. http://dx.doi.org/10.1115/1.3267445.

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This paper discusses Pshenichnyi’s recursive quadratic programming algorithm for use in engineering optimization problems. An evaluation of the original algorithm is offered and several modifications are presented. The modifications include; addition of a variable metric update of the Hessian, an improved active set criterion, direct inclusion of the variable bounds, a divergence control mechanism, and updating schemes for the algorithm parameters. Implementations of the original algorithm and the modified algorithm were tested against the Sandgren test set of 23 engineering optimization problems. The results indicate that the modified algorithm was able to solve 20 of the 23 test problems while the original algorithm solved only 11. The modified algorithm was more efficient than the original on all the test problems.

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O’Donoghue and Heinemann. "Synthetic DNA and RNA Programming." Genes 10, no. 7 (2019): 523. http://dx.doi.org/10.3390/genes10070523.

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Synthetic biology is a broad and emerging discipline that capitalizes on recent advances in molecular biology, genetics, protein and RNA engineering as well as omics technologies. Together these technologies have transformed our ability to reveal the biology of the cell and the molecular basis of disease. This Special Issue on “Synthetic RNA and DNA Programming” features original research articles and reviews, highlighting novel aspects of basic molecular biology and the molecular mechanisms of disease that were uncovered by the application and development of novel synthetic biology-driven approaches.

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Fajfar, Iztok, Janez Puhan, and Árpád Bűrmen. "Evolving a Nelder–Mead Algorithm for Optimization with Genetic Programming." Evolutionary Computation 25, no. 3 (2017): 351–73. http://dx.doi.org/10.1162/evco_a_00174.

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We used genetic programming to evolve a direct search optimization algorithm, similar to that of the standard downhill simplex optimization method proposed by Nelder and Mead ( 1965 ). In the training process, we used several ten-dimensional quadratic functions with randomly displaced parameters and different randomly generated starting simplices. The genetically obtained optimization algorithm showed overall better performance than the original Nelder–Mead method on a standard set of test functions. We observed that many parts of the genetically produced algorithm were seldom or never executed, which allowed us to greatly simplify the algorithm by removing the redundant parts. The resulting algorithm turns out to be considerably simpler than the original Nelder–Mead method while still performing better than the original method.

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Gao, Yuelin, and Siqiao Jin. "A Global Optimization Algorithm for Sum of Linear Ratios Problem." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/276245.

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We equivalently transform the sum of linear ratios programming problem into bilinear programming problem, then by using the linear characteristics of convex envelope and concave envelope of double variables product function, linear relaxation programming of the bilinear programming problem is given, which can determine the lower bound of the optimal value of original problem. Therefore, a branch and bound algorithm for solving sum of linear ratios programming problem is put forward, and the convergence of the algorithm is proved. Numerical experiments are reported to show the effectiveness of the proposed algorithm.

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Feng, Qi Gao, Han Ping Mao, and Hong Wei Jiao. "A Feasible Method for a Class of Mathematical Problems in Manufacturing System." Key Engineering Materials 460-461 (January 2011): 806–9. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.806.

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In this paper a feasible method is proposed for solving a class of mathematical problems in manufacturing system and production system. By utilizing linearization technique the relaxation programming problem about the original problem is constructed. The proposed branch and bound algorithm is convergent to the global minimum of original problem through the successive refinement linear relaxation of the feasible region of objective function and solutions of a series of relaxation linear programming problem. And large number of experiments results show feasibility of presented method.

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Gondzio, Jacek. "On Exploiting Original Problem Data in the Inverse Representation of Linear Programming Bases." ORSA Journal on Computing 6, no. 2 (1994): 193–206. http://dx.doi.org/10.1287/ijoc.6.2.193.

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Ridzuan, M. R. M., E. E. Hassan, A. R. Abdullah, and A. F. A. Kadir. "Sustainable Environmental Economic Dispatch Optimization with Hybrid Metaheuristic Modification." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 1 (2018): 161. http://dx.doi.org/10.11591/ijeecs.v11.i1.pp161-168.

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Today’s Economic Dispatch (ED) solutions are featured with environmental obligations. Hence, the significant objective functions contribute to cost minimization, lower emission and less total system losses. As an alternative, New Meta Heuristic Evolutionary Programming (NMEP) technique was proposed to optimize the individual ED problem categorized as Single Objective Environmental Economic Dispatch (SOEELD), developed from an integration of original Meta Heuristic Evolutionary Programming (Meta-EP) with Artificial Immune System (AIS) with new arrangement in the mutation and cloning processes. The comparative analysis was conducted between the original Meta-EP and classical method of Hadi Saadat to verify the performance of NMEP method. Each particular objective function identified the best possible outcomes through the NMEP method. The simulations were conducted using MATLAB programming which tested both standard IEEE 26 and 57 bus systems.

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Stefanov, Stefan M. "Method for solving a convex integer programming problem." International Journal of Mathematics and Mathematical Sciences 2003, no. 44 (2003): 2829–34. http://dx.doi.org/10.1155/s0161171203210516.

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We consider a convex integer program which is a nonlinear version of the assignment problem. This problem is reformulated as an equivalent problem. An algorithm for solving the original problem is suggested which is based on solving the simple assignment problem via some of known algorithms.

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COLE, MURRAY I. "PARALLEL PROGRAMMING WITH LIST HOMOMORPHISMS." Parallel Processing Letters 05, no. 02 (1995): 191–203. http://dx.doi.org/10.1142/s0129626495000175.

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We review the use of the Bird-Meertens Formalism as a vehicle for the construction of programs with massive implicit parallelism. We show that a simple result from the theory, concerning the expression of list homomorphisms, can help us in our search for parallel algorithms by suggesting an informal methodology which is applicable when the original result is not, and demonstrate its application to a variety of problems. One of these, a language recognition algorithm, produces a program which exploits nested parallelism. Our main purpose is to show that an understanding of the homomorphism lemma can be helpful in producing parallel programs for problems which are "not quite" list homomorphisms themselves. A more general goal is to illustrate the benefits which can arise from taking a little theory with a pinch of pragmatic salt.

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Shi, Can Tao, Zi Sheng Liu, Yu Zhuo Liu, and Yong Liang Zhou. "A Decomposition Algorithm for Byproduct Gases Scheduling in Iron and Steel Enterprises." Applied Mechanics and Materials 448-453 (October 2013): 4609–14. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4609.

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Concentrating on byproduct gas system with multiple pipeline networks, this paper established a mathematical model for byproduct gas scheduling (BGS) problem in iron and steel enterprises with consideration of supply priorities of consumption units. A decomposition algorithm is employed to transform the original model into an integer programming and a linear programming. The genetic algorithm is introduced as the solution mainframe to solve the integer programming problem and the linear programming problem iteratively. The result of practical-data-based experiment indicates that the proposed model and algorithm are feasible and effective.

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GALLAGHER, JOHN, and MICHAEL GELFOND. "Introduction to the 27th International Conference on Logic Programming Special Issue." Theory and Practice of Logic Programming 11, no. 4-5 (2011): 429–32. http://dx.doi.org/10.1017/s1471068411000342.

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Following the initiative in 2010 taken by the Association for Logic Programming and Cambridge University Press, the full papers accepted for the International Conference on Logic Programming again appear as a special issue of Theory and Practice of Logic Programming (TPLP)—the 27th International Conference on Logic Programming Special Issue. Papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere were solicited in all areas of logic programming including but not restricted to: Theory: Semantic Foundations, Formalisms, Non- monotonic Reasoning, Knowledge Representation. Implementation: Compilation, Memory Management, Virtual Machines, Parallelism. Environments: Program Analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing. Language Issues: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Programming Techniques. Related Paradigms: Abductive Logic Programming, Inductive Logic Programming, Constraint Logic Programming, Answer-Set Programming. Applications: Databases, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics.

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McKee, Thomas E. "Altman's 1968 Bankruptcy Prediction Model Revisited via Genetic Programming: New Wine from an Old Bottle or a Better Fermentation Process?" Journal of Emerging Technologies in Accounting 4, no. 1 (2007): 87–101. http://dx.doi.org/10.2308/jeta.2007.4.1.87.

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Prior research raised questions about the information value of some of the variables included in Altman's 1968 seminal bankruptcy prediction model. Answering these questions is of great importance since the original Altman model and variations on it are still used to provide bankruptcy risk signals in accounting and audit practice. This study applied genetic programming to Altman's original data set in order to examine the issue of variable significance. Two parsimonious models employing either one or two variables were developed that equaled the accuracy rate of Altman's five variable model when tested on Altman's original data set. The two variable model also equaled or exceeded the Altman model both when error rates were compared based on prior probabilities of bankruptcy and when relative misclassification costs were considered. The accuracy levels and parsimony of the two genetic programming models supports prior research and confirms that some of the variables in the original model were an artifact of discriminant analysis.

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Achoja, Felix Odemero. "Original Research Article Financial Risk Threshold Determination in Broiler Enterprise in Delta State, Nigeria." Agricultura tropica et subtropica 46, no. 4 (2013): 111–17. http://dx.doi.org/10.2478/ats-2013-0021.

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Abstract Increasing concern about the problem of risk associated with poultry business has highlighted the need for its comprehensive understanding. A clear knowledge of financial risk in broiler enterprise will pave the way to efficient mitigation strategies among broiler producers. This study investigates financial risk programming, and threshold analysis in broiler enterprises in Delta State, Nigeria. Probabilistic (multi-stage) sampling procedure was adopted in selecting 200 broiler farmers for the study. Structured questionnaire was used to collect 6 years time series data (2004-2009) from the respondents. Collected data were analyzed using descriptive statistics, QSB version of linear programming model, and threshold model. The results of the study showed that broiler enterprise is profitable with optimum net profit of N47,925 and N357,558 per small scale and large scale producers, respectively. An optimum profit of N389.9 per bird was earned by broiler producers. The output of QSB version of linear programming showed that at the stocking rate of 20, 692 birds, financial risk is optimized at 15%. The threshold regression model revealed that the broiler enterprise in the study area generally operated below the risk threshold. Simple regression indicated that expected return is positively and significantly (P < 0.05) related with financial risk. Incorporating financial risk as a constraint in the broiler farm plan is a useful contribution that will enhance efficient farm planning. The optimal return and financial risk threshold provided in this study will improve the confidence level of stakeholders in poultry industry such as current and potential investors, insurance institution and institutional lenders. This will translate to growth in the broiler subsector of the poultry industry in Delta State

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Wang, Xiao Guang, and Chun Juan Hou. "An Entropy Function Implementation of Quadratic Programming." Applied Mechanics and Materials 226-228 (November 2012): 2227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2227.

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This paper is intended to describe a new algorithm, which makes entropy function method with Lagrange function and Taylor formula for solving inseparable variables of quadratic programming. Quadratic programming problem are an important in the fields of nonlinear programming problem. Entropy function also called KS function. The nature and related certificate of KS function and its convergence have already been proved at home and abroad. The application of KS function nature for solving quadratic programming is a very good method, and it is one of the advantages of making more constraint programming problem become a single constraint programming problem, and the original problems are simplified. Electing three examples of separated variables for quadratic programming problems, that is cross terms of zero, and then contrasted with the new method. The algorithm resolves implementation of separated variables of quadratic programming. Our numerical experiments show the proposed algorithm is feasible.

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Jana, Mrinal, and Geetanjali Panda. "MULTI-OBJECTIVE GEOMETRIC PROGRAMMING WITH VARYING PARAMETERS: APPLICATION IN WASTE WATER TREATMENT SYSTEM." Mathematical Modelling and Analysis 20, no. 5 (2015): 585–603. http://dx.doi.org/10.3846/13926292.2015.1087889.

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In this paper a general multi-objective geometric programming problem with interval parameters is proposed, and a methodology is developed to derive its solution. The model is transformed into a general geometric programming problem, and relation between the original problem and the transformed problem is established. Application of this discussion is illustrated in a waste water treatment model.

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Lucenko, Alexandr V. "Optimization of hydraulic modes of distribution heat networks by dynamic programming." E3S Web of Conferences 39 (2018): 03003. http://dx.doi.org/10.1051/e3sconf/20183903003.

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An original modification of the dynamic programming method is proposed, which is designed to optimize the hydraulic modes of distribution heat networks, based on special properties of the problem. It is demonstrated that the proposed modification of the dynamic programming method has a high computational efficiency in comparison with possible alternative methods of discrete-continuous optimization.

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MANCARELLA, PAOLO, GIACOMO TERRENI, FARIBA SADRI, FRANCESCA TONI, and ULLE ENDRISS. "The CIFF proof procedure for abductive logic programming with constraints: Theory, implementation and experiments." Theory and Practice of Logic Programming 9, no. 6 (2009): 691–750. http://dx.doi.org/10.1017/s1471068409990093.

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AbstractWe present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state-of-the-art abductive systems and answer set solvers and showing how to use it to program some applications.

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Acakpovi, Amevi. "Original Framework for Optimizing Hybrid Energy Supply." Journal of Energy 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8317505.

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This paper proposes an original framework for optimizing hybrid energy systems. The recent growth of hybrid energy systems in remote areas across the world added to the increasing cost of renewable energy has triggered the inevitable development of hybrid energy systems. Hybrid energy systems always pose a problem of optimization of cost which has been approached with different perspectives in the recent past. This paper proposes a framework to guide the techniques of optimizing hybrid energy systems in general. The proposed framework comprises four stages including identification of input variables for energy generation, establishment of models of energy generation by individual sources, development of artificial intelligence, and finally summation of selected sources. A case study of a solar, wind, and hydro hybrid system was undertaken with a linear programming approach. Substantial results were obtained with regard to how load requests were constantly satisfied while minimizing the cost of electricity. The developed framework gained its originality from the fact that it has included models of individual sources of energy that even make the optimization problem more complex. This paper also has impacts on the development of policies which will encourage the integration and development of renewable energies.

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LEUSCHEL, MICHAEL, and TOM SCHRIJVERS. "Introduction to the 30th International Conference on Logic Programming Special Issue." Theory and Practice of Logic Programming 14, no. 4-5 (2014): 401–14. http://dx.doi.org/10.1017/s1471068414000581.

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The 30th edition of the International Conference of Logic Programming took place in Vienna in July 2014 at the Vienna Summer of Logic - the largest scientific conference in the history of logic. Following the initiative in 2010 taken by the Association for Logic Programming and Cambridge University Press, the full papers accepted for the International Conference on Logic Programming again appear as a special issue of Theory and Practice of Logic Programming (TPLP) - the 30th International Conference on Logic Programming Special Issue. Papers describing original, previously unpublished research and not simultaneously submitted for publication elsewhere were solicited in all areas of logic programming including but not restricted to: Theory: Semantic Foundations, Formalisms, Non- monotonic Reasoning, Knowledge Representation; Implementation: Compilation, Memory Management, Virtual Machines, Parallelism; Environments: Program Analysis, Transformation, Validation, Verification, Debugging, Profiling, Testing; Language Issues: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Programming Techniques; Related Paradigms: Abductive Logic Programming, Inductive Logic Programming, Constraint Logic Programming, Answer-Set Programming; Applications: Databases, Data Integration and Federation, Software Engineering, Natural Language Processing, Web and Semantic Web, Agents, Artificial Intelligence, Bioinformatics.

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Zhu, Bo, Xiang Ling Cao, Yu Wang, and Qiang Gao. "Constraint Programming Method for Crew Schedule Recovery." Applied Mechanics and Materials 496-500 (January 2014): 1788–91. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1788.

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Unexpected disruptions such as aircraft failure and airport closure often make the original flight schedule cannot operate regularly and destroy the crew duties. This paper proposed a constraint programming model to solve the crew recovery problem. The total recovery cost was taken as the objective function, temporal-spacial requirements, deadheading and time legalities were considered as constraints. An algorithm based on sequential, least slack and greedy thoughts was designed to search the solution space. Finally, an example was test to indicated feasibility of the proposed model and algorithm.

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Zhang, Liwei, Shengzhe Gao, and Saoyan Guo. "Statistical Inference of Second-Order Cone Programming." Asia-Pacific Journal of Operational Research 36, no. 02 (2019): 1940003. http://dx.doi.org/10.1142/s0217595919400037.

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In this paper, we study the stability of stochastic second-order programming when the probability measure is perturbed. Under the Lipschitz continuity of the objective function and metric regularity of the feasible set-valued mapping, the outer semicontinuity of the optimal solution set and Lipschitz continuity of optimal values are demonstrated. Moreover, we prove that, if the constraint non-degeneracy condition and strong second-order sufficient condition hold at a local minimum point of the original problem, there exists a Lipschitz continuous solution path satisfying the Karush–Kuhn–Tucker conditions.

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Wang, Zhi-Cheng, and Xiao-Bei Wu. "Hybrid Biogeography-Based Optimization for Integer Programming." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/672983.

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Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems.

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Atwood, Joseph, Larry J. Held, Glenn A. Helmers, and Myles J. Watts. "Performance of Risk-Income Models Outside the Original Data Set." Journal of Agricultural and Applied Economics 18, no. 2 (1986): 113–24. http://dx.doi.org/10.1017/s0081305200006166.

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AbstractSelected risk programming solutions (i.e., profit maximization, Target-MOTAD, and MOTAD) are tested in an economic environment outside the data set from which they were developed. Specifically, solutions are derived from either a longer 10-year (1965-74) or shorter 6-year estimation period (1969-74), and then, they are tested for consistent risk-income characteristics over a later 10-year period (1975-84). Risk solutions estimated from earlier periods perform well in the later test period in spite of different economic conditions between time periods. However, favorable performance may be related to the specific example used in this analysis. Further testing for other farm situations is needed before general conclusions can be reached.

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Zheng, Yue, Jiawei Chen, and Xiaogang Cao. "A global solution method for semivectorial bilevel programming problem." Filomat 28, no. 8 (2014): 1619–27. http://dx.doi.org/10.2298/fil1408619z.

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In this paper, we consider a class of semivectorial bilevel programming problem. An exact penalty function is proposed for such a problem. Based on this penalty function, an algorithm, which can obtain a global solution of the original problem, is presented. Finally, some numerical results illustrate its feasibility.

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Van Hulle, M. M. "A GOAL PROGRAMMING NETWORK FOR MIXED INTEGER LINEAR PROGRAMMING: A CASE STUDY FOR THE JOB-SHOP SCHEDULING PROBLEM." International Journal of Neural Systems 02, no. 03 (1991): 201–9. http://dx.doi.org/10.1142/s0129065791000182.

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Job-shop scheduling is an np-complete optimization problem subject to precedence and resource constraints. Recently, Foo and Takefuji have introduced a network-based solution procedure for solving job-shop problems formulated as mixed integer linear programming problems. To obtain the solution, the Tank and Hopfield linear programming network was repeatedly used. However, since such a network frequently produces constraint-violating solutions, the reliability of Foo and Takefuji’s approach is doubtful. In this article, it is shown that reliability of the network approach can be greatly improved, by guaranteeing constraint-satisfying solutions, if the original job-shop problem is reformulated as a goal programming problem, before it is mapped onto a goal programming network.

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Walter, Matthias. "The Graphical Traveling Salesperson Problem has no integer programming formulation in the original space." Operations Research Letters 49, no. 4 (2021): 623–24. http://dx.doi.org/10.1016/j.orl.2021.06.015.

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Sheng, Ze Hong, Wan Hong Xu, Wen Hui Wang, and Yong Xiang Li. "Research and Development of Intelligent Programming Technology of LGMazak Turning Center." Key Engineering Materials 621 (August 2014): 588–93. http://dx.doi.org/10.4028/www.scientific.net/kem.621.588.

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In order to fit the intelligent development of NC programming system, through in-depth study to master the internal structure and structural principle of NC turning center, this paper developed the innovative NC programming model using common G-code based on its original MAZATROL conversational programming capabilities, and further implements the G-code programming model embedded into its specified MAZATROL conversational programming model, which not only can reflect the versatility of the common G-code programming model, but also can bring into play simple and easy maneuverability of MAZATROL programming model; Finally, by running NC program to perform the trial turning test on NC turning center, the test results verify that the nested NC programming model developed by this paper can effectively solve the tedious and error-prone manual NC programming problem, which greatly reduces the programming and debugging time, improves utilization of NC machine tools, and ultimately has brought significant economic benefits. Therefore, the research results of the paper will provide a simple and effective and practical way to improve the programming efficiency, and have important application value to further enhance the programming intuitive research of NC turning center.

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36

Brumbaugh, David L. "SMAP revisited and revised." GEOPHYSICS 57, no. 2 (1992): 258–62. http://dx.doi.org/10.1190/1.1443238.

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The original seismic mis‐tie adjustment procedure (SMAP) is restricted to uniform grids of 2-D seismic lines. In many areas, uniform grids do not occur, especially with multiple‐vintage coverage. An alternative method is proposed still using linear programming techniques to simultaneously minimize the maximum error and absolute error sum, but extending the technique to handle any 2-D seismic grid orientation. Horizons can be treated individually or in combination, with certain restrictions. The original procedure also has some poorly imposed constraints. SAS/OR™ software improves upon the original SMAP results. A comparison is made between a 2-D seismic interpretation corrected with the new algorithm and a 3-D seismic interpretation covering the same area, showing an excellent correlation between the two structural pictures. 2-D seismic interpretation is still a viable tool, and improved maps can be made through this linear programming technique for minimizing mis‐ties.

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Chun, Seok-Ju, Yunju Jo, and Seungmee Lee. "The Effect of Programming Classes with Tangible Scratch Blocks on the Programming Interest of 6th Grade Elementary School Students." International Journal of Information and Education Technology 11, no. 9 (2021): 405–9. http://dx.doi.org/10.18178/ijiet.2021.11.9.1542.

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In this paper, we introduce an original, classroom-based approach for teaching Scratch programming to 6th grade elementary school students. Scratch is a programming language that involves assembling icon-based command blocks. It was designed to avoid the complex syntax errors seen in other programming languages, making it especially accessible for younger learners. While Scratch does provide a visual programming environment in which potentially just about anyone can learn to read and write programming code, there can still be a reduced overall interest in learning programming, because younger learners in particular can find it difficult to intuitively understand or be stimulated by abstract concepts of programming such as sequences, conditions, and repetition, which are present in Scratch. Our research involves the development of a tangible, electronic block system that allows students to manipulate physical objects with their hands to perform programming tasks. The system consists of a Scratch simulator and physical, Scratch electronic blocks embodying Scratch user interface shapes. We devised and delivered a programming course to 6th grade Korean elementary school students using our block system. The results are encouraging.

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Zhang, Haiqing, Aicha Sekhari, Yacine Ouzrout, and Abdelaziz Bouras. "Optimal Inconsistency Repairing of Pairwise Comparison Matrices Using Integrated Linear Programming and Eigenvector Methods." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/989726.

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Satisfying consistency requirements of pairwise comparison matrix (PCM) is a critical step in decision making methodologies. An algorithm has been proposed to find a new modified consistent PCM in which it can replace the original inconsistent PCM in analytic hierarchy process (AHP) or in fuzzy AHP. This paper defines the modified consistent PCM by the original inconsistent PCM and an adjustable consistent PCM combined. The algorithm adopts asegment treeto gradually approach the greatest lower bound of the distance with the original PCM to obtain the middle value of an adjustable PCM. It also proposes a theorem to obtain the lower value and the upper value of an adjustable PCM based on two constraints. The experiments for crisp elements show that the proposed approach can preserve more of the original information than previous works of the same consistent value. The convergence rate of our algorithm is significantly faster than previous works with respect to different parameters. The experiments for fuzzy elements show that our method could obtain suitable modified fuzzy PCMs.

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WONG, W. ERIC, and JENNY LI. "REDESIGNING LEGACY SYSTEMS INTO THE OBJECT-ORIENTED PARADIGM." International Journal of Software Engineering and Knowledge Engineering 14, no. 03 (2004): 255–76. http://dx.doi.org/10.1142/s0218194004001634.

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Object-oriented languages support many modern programming concepts such as information hiding, inheritance, polymorphism, and dynamic binding. As a result, software systems implemented in OO languages are in general more reusable and reliable than others. Many legacy software systems, created before OO programming became popular, need to be redesigned and updated to OO programs. The process of abstracting OO designs from the procedural source code has often been done with limited assistance from program structural diagrams. Most reengineering focuses on the functionality of the original program, and the OO redesign often results in a completely new design based on the designers' understanding of the original program. Such an approach is not sufficient because it may take a significant amount of time and effort for designers to comprehend the original program. This paper presents a computer-aided semi-automatic method that abstracts OO designs from the original procedural source code. More specifically, it is a method for OO redesign based on program structural diagrams, visualization, and execution slices. We conducted a case study by applying this method to an inventory management software system. Results indicate that our method can effectively and efficiently abstract an appropriate OO design out of the original C code. In addition, some of the code from the original system can be automatically identified and reused in the new OO system.

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Wu, Xi Shuang, Tie Lin Liu, and Wen Bo Chen. "Optimization Design for Steel Structure Residence Based on the Sequential Quadratic Programming Method." Applied Mechanics and Materials 256-259 (December 2012): 680–83. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.680.

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In order to reduce the steel consumption for one of the steel structure residence, a kind of effective approach of optimization design to the original design is given. In this paper, steel consumption of H-section steel beams are taken as objective function, which have relatively larger proportions than others. Design variables are the beam height, web thickness, flange width and flange thickness. The strength, stiffness and stability of the steel beam are taken as constraint condition under the relevant Building Code. By using the sequential quadratic programming method, we set up optimization mathematical model for optimizing the steel structure residence, and then obtain the optimal section size of the steel beams. After the structural optimization design, the steel consumption of GL1 was reduced by 8.24%, compared with those of the original design. Relative to the original design, optimized total steel consumption was reduced by 3.29%. The approach proposed in this paper is an effective method for the optimization design of steel structure residence.

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Li, He Cheng, and Yu Ping Wang. "A Genetic Algorithm for Solving Linear-Quadratic Bilevel Program-Ming Problems." Advanced Materials Research 186 (January 2011): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amr.186.626.

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In this paper, we focus on a special linear-quadratic bilevel programming problem in which the follower’s problem is a convex-quadratic programming, whereas the leader’s functions are linear. At first, based on Karush-Kuhn-Tucher(K-K-T) conditions, the original problem is transformed into an equivalent nonlinear programming problem in which the objective and constraint functions are linear except for the complementary slack conditions. Then, a genetic algorithm is proposed to solve the equivalent problem. In the proposed algorithm, the individuals are encoded in two phases. Finally, the efficiency of the approach is demonstrated by an example.

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Jia, Shi Hui, and Yue Zheng. "A New Coordination Model for Ill-Posed Bilevel Programming Problem." Applied Mechanics and Materials 411-414 (September 2013): 1943–47. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1943.

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This paper studied the leader how to motivate the follower to maximize its interests for ill-posed linear bilevel programming problem. We first presented a coordination model and gived its corresponding penalty problem. Under some conditions, we established the result on the existence of the solution. Then, an algorithm was developed to obtain a coordination solution to the original bilevel programming problem. Finally, numerical results show that the proposed method is feasible.

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Stetsyuk, Petro, Andreas Fischer, and Olha Khomiak. "Maximum Penalty Function in Linear Programming." Physico-mathematical modelling and informational technologies, no. 33 (September 6, 2021): 156–60. http://dx.doi.org/10.15407/fmmit2021.33.156.

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A linear program can be equivalently reformulated as an unconstrained nonsmooth minimization problem, whose objective is the sum of the original objective and a penalty function with a sufficiently large penalty parameter. The article presents two methods for choosing this parameter. The first one applies to linear programs with usual linear inequality constraints. Then, we use a corresponding theorem by N.Z. Shor on the equivalence of a convex program to an unconstrained nonsmooth minimization problem. The second method is for linear programs of a special type. This means that all inequalities are of the form that a linear expression on the left-hand side is less or equal to a positive constant on the right-hand side. For this special type, we use a corresponding theorem of B.N. Pshenichny on establishing a penalty parameter for convex programs. For differently sized linear programs of the special type, we demonstrate that suitable penalty parameters can be computed by a procedure in GNU Octave based on GLPK software.

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Sitek, Pawel, and Jaroslaw Wikarek. "A Hybrid Method for the Modelling and Optimisation of Constrained Search Problems." Foundations of Management 5, no. 3 (2014): 7–22. http://dx.doi.org/10.2478/fman-2014-0016.

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AbstractThe paper presents a concept and the outline of the implementation of a hybrid approach to modelling and solving constrained problems. Two environments of mathematical programming (in particular, integer programming) and declarative programming (in particular, constraint logic programming) were integrated. The strengths of integer programming and constraint logic programming, in which constraints are treated in a different way and different methods are implemented, were combined to use the strengths of both. The hybrid method is not worse than either of its components used independently. The proposed approach is particularly important for the decision models with an objective function and many discrete decision variables added up in multiple constraints. To validate the proposed approach, two illustrative examples are presented and solved. The first example is the authors’ original model of cost optimisation in the supply chain with multimodal transportation. The second one is the two-echelon variant of the well-known capacitated vehicle routing problem.

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Climent, L., R. J. Wallace, M. A. Salido, and F. Barber. "Robustness and Stability in Constraint Programming under Dynamism and Uncertainty." Journal of Artificial Intelligence Research 49 (January 28, 2014): 49–78. http://dx.doi.org/10.1613/jair.4126.

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Many real life problems that can be solved by constraint programming, come from uncertain and dynamic environments. Because of the dynamism, the original problem may change over time, and thus the solution found for the original problem may become invalid. For this reason, dealing with such problems has become an important issue in the fields of constraint programming. In some cases, there exist extant knowledge about the uncertain and dynamic environment. In other cases, this information is fragmentary or unknown. In this paper, we extend the concept of robustness and stability for Constraint Satisfaction Problems (CSPs) with ordered domains, where only limited assumptions need to be made as to possible changes. We present a search algorithm that searches for both robust and stable solutions for CSPs of this nature. It is well-known that meeting both criteria simultaneously is a desirable objective for constraint solving in uncertain and dynamic environments. We also present compelling evidence that our search algorithm outperforms other general-purpose algorithms for dynamic CSPs using random instances and benchmarks derived from real life problems.

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SINGH, SATNAM, and ROBERT BRUCE FINDLER. "Special Issue Dedicated to ICFP 2012: Editorial." Journal of Functional Programming 24, no. 2-3 (2014): 131–32. http://dx.doi.org/10.1017/s0956796814000124.

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The 17th ACM SIGPLAN International Conference on Functional Programming (ICFP) took place on September 10–12, 2012 in Copenhagen, Denmark. After the conference, the programme committee selected several outstanding papers and invited their authors to submit to this special issue of Journal of Functional Programming. Robby Findler and Satnam Singh acted as editors for these submissions. This issue includes the accepted papers, each of which provides substantial new material beyond the original conference version.

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Liu, X., Y. L. Gao, B. Zhang, and F. P. Tian. "A New Global Optimization Algorithm for a Class of Linear Fractional Programming." Mathematics 7, no. 9 (2019): 867. http://dx.doi.org/10.3390/math7090867.

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In this paper, we propose a new global optimization algorithm, which can better solve a class of linear fractional programming problems on a large scale. First, the original problem is equivalent to a nonlinear programming problem: It introduces p auxiliary variables. At the same time, p new nonlinear equality constraints are added to the original problem. By classifying the coefficient symbols of all linear functions in the objective function of the original problem, four sets are obtained, which are     I  i  +    ,     I  i  −    ,     J  i  +    and     J  i  −    . Combined with the multiplication rule of real number operation, the objective function and constraint conditions of the equivalent problem are linearized into a lower bound linear relaxation programming problem. Our lower bound determination method only needs      e i T  x +  f i  ≠ 0    , and there is no need to convert molecules to non-negative forms in advance for some special problems. A output-space branch and bound algorithm based on solving the linear programming problem is proposed and the convergence of the algorithm is proved. Finally, in order to illustrate the feasibility and effectiveness of the algorithm, we have done a series of numerical experiments, and show the advantages and disadvantages of our algorithm by the numerical results.

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Milik, Adam. "Multiple-Core PLC CPU Implementation and Programming." Journal of Circuits, Systems and Computers 27, no. 10 (2018): 1850162. http://dx.doi.org/10.1142/s0218126618501621.

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The paper presents a complete approach to the multithreaded execution of a control program prepared according to IEC61131-3 standard. The program is mapped to a dedicated multiple-core CPU unit. The CPU consists of multiple independent bit and word CPUs. The computation synchronization mechanism is based on memory cells with semaphored access, which enable hardware-level synchronization. The paper presents in detail the architecture, results of implementation and the achieved performance. The custom-developed compiler translates standard programming languages into a multithreaded executable form. It utilizes an original intermediate data flow graph to optimize and recognize program parallelisms. The program is automatically partitioned and mapped to the available computing resources. The paper is concluded with a performance comparison of program executions using the standard single-threaded and proposed approaches.

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Wu, Bo, Meng Jun Li, Bin Zhang, Chao Feng, Quan Zhang, and Chao Jing Tang. "Software Vulnerabilities Detection Based on Random Programming." Applied Mechanics and Materials 571-572 (June 2014): 553–58. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.553.

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Despite many automatic vulnerability detection approaches have been well documented, existing solutions for discovering software vulnerabilities in binary software are still difficult and time consuming. In this paper we present an approach based on random programming that works to quickly discover vulnerability in programmable binary software. By extracting the code snippets for special features and fixed API usages, we can get a set of original functional templates, and then we randomize the mutable factors in those templates. After that we reasonably make combination of those templates to produce final test templates. Finally, by concretizing the random factors we execute those test templates and monitor the software be tested to discover vulnerabilities. By template programming we can produce more reasonable test case, which makes our approach more effective than other solutions.

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Lin, Ming-Hua, John Gunnar Carlsson, Dongdong Ge, Jianming Shi, and Jung-Fa Tsai. "A Review of Piecewise Linearization Methods." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/101376.

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Various optimization problems in engineering and management are formulated as nonlinear programming problems. Because of the nonconvexity nature of this kind of problems, no efficient approach is available to derive the global optimum of the problems. How to locate a global optimal solution of a nonlinear programming problem is an important issue in optimization theory. In the last few decades, piecewise linearization methods have been widely applied to convert a nonlinear programming problem into a linear programming problem or a mixed-integer convex programming problem for obtaining an approximated global optimal solution. In the transformation process, extra binary variables, continuous variables, and constraints are introduced to reformulate the original problem. These extra variables and constraints mainly determine the solution efficiency of the converted problem. This study therefore provides a review of piecewise linearization methods and analyzes the computational efficiency of various piecewise linearization methods.

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Journal articles on the topic 'Original programming'

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