The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
P J Fleming - One of the best experts on this subject based on the ideXlab platform.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms
Dagstuhl Seminar Proceedings, 2005Co-Authors: Carlos M Fonseca, P J FlemingAbstract:In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including Constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms ii application example
Systems Man and Cybernetics, 1998Co-Authors: Carlos M Fonseca, P J FlemingAbstract:For part I see ibid., 26-37. The evolutionary approach to Multiple function optimization formulated in the first part of the paper is applied to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine. This study illustrates how a technique such as the multiobjective genetic algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. Several objective functions and associated goals express design concerns in direct form, i.e., as the designer would state them. While such a designer-oriented formulation is very attractive, its practical usefulness depends heavily on the ability to search and optimize cost surfaces in a class much broader than usual, as already provided to a large extent by the genetic algorithm (GA). The two instances of the problem studied demonstrate the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively. It is shown that only a very small portion of the nondominated set is of practical relevance, which further substantiates the need to supply preference information to the GA. The paper concludes with a discussion of the results.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms i a unified formulation
Systems Man and Cybernetics, 1998Co-Authors: Carlos M Fonseca, P J FlemingAbstract:In optimization, Multiple objectives and Constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process. While "better" solutions should be rated higher than "worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of Multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions. After reviewing current revolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.
Carlos M Fonseca - One of the best experts on this subject based on the ideXlab platform.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms
Dagstuhl Seminar Proceedings, 2005Co-Authors: Carlos M Fonseca, P J FlemingAbstract:In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including Constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms ii application example
Systems Man and Cybernetics, 1998Co-Authors: Carlos M Fonseca, P J FlemingAbstract:For part I see ibid., 26-37. The evolutionary approach to Multiple function optimization formulated in the first part of the paper is applied to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine. This study illustrates how a technique such as the multiobjective genetic algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. Several objective functions and associated goals express design concerns in direct form, i.e., as the designer would state them. While such a designer-oriented formulation is very attractive, its practical usefulness depends heavily on the ability to search and optimize cost surfaces in a class much broader than usual, as already provided to a large extent by the genetic algorithm (GA). The two instances of the problem studied demonstrate the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively. It is shown that only a very small portion of the nondominated set is of practical relevance, which further substantiates the need to supply preference information to the GA. The paper concludes with a discussion of the results.
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multiobjective optimization and Multiple Constraint handling with evolutionary algorithms i a unified formulation
Systems Man and Cybernetics, 1998Co-Authors: Carlos M Fonseca, P J FlemingAbstract:In optimization, Multiple objectives and Constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and differentiability of the cost surface add yet another conflicting element to the decision process. While "better" solutions should be rated higher than "worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of Multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or fitness, to such candidate solutions. After reviewing current revolutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.
Yong Shi - One of the best experts on this subject based on the ideXlab platform.
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a study on error correction of Multiple criteria and Multiple Constraint levels linear programming based classification
Proceedings of the International Conference on Web Intelligence, 2017Co-Authors: Bo Wang, Yong ShiAbstract:In credit card client classification problem, reducing misclassified rate is regarded as a key issue. Unfortunately, existing machine learning methods cannot be successfully applied to this problem. This paper introduces a classification model based on Multiple criteria and Multiple Constraint levels linear programming (MC2LP), which equips two intervals of cutoff in the model. Two parallel hyperplanes are employed to indicate the relative positions between the points and hyperplanes. Then, we discuss the correctness of new model in error correction. Matrix representations of relevant models are also offered. Finally, compared to original MCLP, known MC2LP, Logistic Regression (LR) and Support Vector Machine (SVM), the propsed model shows superiority in solving two types of error related data mining problem.
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Error Correction Method in Classification by Using Multiple-Criteria and Multiple-Constraint Levels Linear Programming
International Journal of Computers Communications & Control, 2014Co-Authors: Bo Wang, Yong ShiAbstract:In classification based on Multiple-criteria linear programming (MCLP), we need to find the optimal solution of the MCLP problem as a classifier. According to dual theory, Multiple criteria can be switched to Multiple Constraint levels, and vice versa. A MCLP problem can be logically extended into a Multiple-criteria and Multiple-Constraint levels linear programming (MC2LP) problem. In many applications, such as credit card account classification, how to handle two types of error is a key issue. The errors can be caused by a fixed cutoff between a "Good" group and a "Bad" group. Two types of error can be systematically corrected by using the structure of MC2LP, which allows two alterable cutoffs. In order to do so, a penalty (or cost) is imposed to find the potential solution for all possible trade-offs in solving MC2LP problem. Some correction strategies can be investigated by the solution procedure. Furthermore, a framework of decision supporting system can be illustrated for various real-life applications of the proposed method.
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domain driven classification based on Multiple criteria and Multiple Constraint level programming for intelligent credit scoring
IEEE Transactions on Knowledge and Data Engineering, 2010Co-Authors: Yanchun Zhang, Yong Shi, Guangyan HuangAbstract:Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of Multiple criteria and Multiple Constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and Constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods.
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Human Resource Allocation in a CPA Firm: A Fuzzy Set Approach
Review of Quantitative Finance and Accounting, 2003Co-Authors: Wikil Kwak, Yong Shi, Kooyul JungAbstract:The review of existing human resource allocation models for a CPA firm shows that there are major shortcomings in the previous mathematical models. First, linear programming models cannot handle Multiple objective human resource allocation problems for a CPA firm. Second, goal programming or Multiple objective linear programming (MOLP) cannot deal with the organizational differentiation problems. To reduce the complexity in computing the trade-offs among Multiple objectives, this paper adopts a fuzzy set approach to solve human resource allocation problems. A solution procedure is proposed to systematically identify a satisfying selection of possible staffing solutions that can reach the best compromise value for the Multiple objectives and Multiple Constraint levels. The fuzzy solution can help the CPA firm make a realistic decision regarding its human resource allocation problems as well as the firm's overall strategic resource management when environmental factors are uncertain.
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Multiple criteria and Multiple Constraint levels linear programming concepts techniques and applications
2001Co-Authors: Yong Shi, Yi PengAbstract:Part 1 Basic concepts and techniques: basic techniques of linear programming relations between LP, MC and MC2 problems MC2 linear programming. Part 2 Special MC2 problems: MC2 integer problems MC2 transportation problems. Part 3 Fuzzy MC2 solutions: fuzzy MC2 problems duality of fuzzy MC2 problems. Part 4 Optimal linear designs: optimal system designs and contingency plans generalized good system and contingency plans optimal system designs and dual contingency plans generalized good systems and dual contingency plans with computer-aided system. Part 5 Related approaches: satisficing and compromise models de novo programming with Multiple criteria eliminating permanently dominated opportunity. Part 6 Applications: optimal trade-off analysis in transfer pricing capital budgeting with Multiple criteria and Multiple decision makers data-file allocations rural telecommunications system credit-card portfolio management aggregate production planning agricultural policy making.
Herschel Rabitz - One of the best experts on this subject based on the ideXlab platform.
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Frequency domain quantum optimal control under Multiple Constraints
Physical Review A, 2016Co-Authors: Chuan-cun Shu, Xi Xing, Herschel RabitzAbstract:© 2016 American Physical Society. Optimal control of quantum systems with complex constrained external fields is one of the longstanding theoretical and numerical challenges at the frontier of quantum control research. Here, we present a theoretical method that can be utilized to optimize the control fields subject to Multiple Constraints while guaranteeing monotonic convergence towards desired physical objectives. This optimization method is formulated in the frequency domain in line with the current ultrafast pulse shaping technique, providing the possibility for performing quantum optimal control simulations and experiments in a unified fashion. For illustrations, this method is successfully employed to perform Multiple Constraint spectral-phase-only optimization for maximizing resonant multiphoton transitions with desired pulses
Akira Oyama - One of the best experts on this subject based on the ideXlab platform.
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adaptively preserving solutions in both feasible and infeasible regions on generalized Multiple Constraint ranking
Genetic and Evolutionary Computation Conference, 2020Co-Authors: Yohanes Bimo Dwianto, Hiroaki Fukumoto, Akira OyamaAbstract:In the present work, we propose some new modifications of an existing Constraint handling technique (CHT) for single-objective optimization problems. The base CHT is generalized Multiple Constraint ranking (G-MCR), which is already a modified version of the original CHT, MCR. Despite that G-MCR significantly outperformed the original MCR in the previous study, it is found that G-MCR tends to generate very few feasible individuals on a certain real-world like engineering design problem. In the present work, G-MCR is further modified to strike a better balance between feasible and infeasible individuals on each generation in an adaptive way so that the interaction between feasible and infeasible regions can be maintained, thus providing more efficient search towards constrained global optimum. Based on the investigation on 78 benchmark problems, we obtain that some of the proposed modifications produce more robust convergence performance by obtaining significant superiority on many types of problems. On real-world like engineering design problems, we also observe that the feasibility ratio generated on each generation might have an important role in improving the convergence performance.
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on improving the Constraint handling performance with modified Multiple Constraint ranking mcr mod for engineering design optimization problems solved by evolutionary algorithms
Genetic and Evolutionary Computation Conference, 2019Co-Authors: Yohanes Bimo Dwianto, Hiroaki Fukumoto, Akira OyamaAbstract:This work presents a new rank-based Constraint handling technique (CHT) by implementing a modification on Multiple Constraint Ranking (MCR), a recently proposed rank-based Constraint handling technique (CHT) for real-world engineering design optimization problems solved by evolutionary algorithms. The new technique, namely MCR-mod, not only maintains MCR's superior feature, i.e. balanced assessment of Constraints with different orders of magnitude and/or different units, but also adds some more good features, such as more proper rank definition that the best feasible solution in the population always has better rank than the best infeasible solution, involvement of good infeasible solution, and easier way of implementation. Numerical experiments on benchmark problems from IEEE-CEC 2006 competition and engineering design are conducted to assess the accuracy and robustness of MCR-mod. From 25 independent runs on each problem, MCR-mod has proven its robustness compared to MCR, by its ability to produce better feasible optimal solution in most problems. Based on nonparametric statistical tests, there are indications that MCR-mod yields significant superiority in terms of accuracy compared with MCR on problems whose most Constraints are inequality and active Constraints, indicating that all added features of MCR-mod produce some improvements on the Constraint-handling performance.
