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HYBRID GENETIC PROGRAMMING-BASED search algorithms FOR ENTERPRISE BANKRUPTCY PREDICTION

APPLIED ARTIFICIAL INTELLIGENCE 2011年第8期25卷 669-692页

作者： Divsalar, Mehdi Javid, Mohamad Rezi Gandomi, Amir Hosein Soofi, Jahaniar Bamdad Mahmood, Majid Vesali Allameh Tabatabaei Univ Fac Management & Accounting Tehran Iran Tehran Municipalitys Beautificat Org Tehran Iran Tafresh Univ Coll Civil Engn Tafresh Iran Iran Univ Sci & Technol Sch Math Tehran Iran

Bankruptcy is an extremely significant worldwide problem that affects the economic well-being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Utilizing the hybrid GP/OLS and GP/SA techniques, generalized relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.

关键词： GENETIC programming (Computer science) search algorithms BANKRUPTCY EXTERNALITIES (Economics) FINANCIAL ratios PREDICTION theory LEAST squares

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Wind Farm Layout Design Using Cuckoo search algorithms

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APPLIED ARTIFICIAL INTELLIGENCE 2016年第10期30卷 899-922页

作者： Rehman, S. Ali, S. S. Khan, S. A. King Fahd Univ Petr & Minerals Res Inst Ctr Engn Res Dhahran Saudi Arabia Univ Teknol PETRONAS Elect & Elect Engn Dept Bandar Seri Iskandar Tronoh Malaysia Univ Pretoria Comp Sci Dept Pretoria South Africa

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristicbased seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.

关键词： WIND power plants PLANT layout search algorithms PARTICLE swarm optimization EMPIRICAL research ENERGY consumption

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Geometric pictures for quantum search algorithms

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QUANTUM INFORMATION PROCESSING 2012年第2期11卷 325-340页

作者： Zhao, Lian-Jie Li, Yan-Song Hao, Liang Zhou, Tao Long, Gui Lu Tsinghua Univ Dept Phys Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Key Lab Atom & Mol Nanosci Beijing 100084 Peoples R China

In this paper, we give the geometric pictures for quantum search algorithms in decomposed form, namely in terms of a phase rotation of the marked state and a phase rotation about the average. We apply this formalism to various quantum search algorithms, and give explicit interpretations of the standard Grover algorithm, arbitrary phase rotations, phase matching and fixed-point search algorithm. The pictures straightforwardly show how state vectors evolve during the search process. These results are helpful in understanding how the quantum search algorithms work.

关键词： QUANTUM information science search algorithms GEOMETRIC analysis DECOMPOSITION (Mathematics) FORMS (Mathematics) MATCHING theory

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A study of search algorithms' optimization speed

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2014年第2期27卷 256-270页

作者： Valsecchi, Andrea Vanneschi, Leonardo Mauri, Giancarlo European Ctr Soft Comp Mieres Spain Univ Milano Bicocca DISCo I-20126 Milan Italy Univ Nova Lisboa ISEGI P-1070312 Lisbon Portugal

search algorithms are often compared by the optimization speed achieved on some sets of cost functions. Here some properties of algorithms' optimization speed are introduced and discussed. In particular, we show that determining whether a set of cost functions F admits a search algorithm having given optimization speed is an NP-complete problem. Further, we derive an explicit formula to calculate the best achievable optimization speed when F is closed under permutation. Finally, we show that the optimization speed achieved by some well-know optimization techniques can be much worse than the best theoretical value, at least on some sets of optimization benchmarks.

关键词： Optimization problems search algorithms Optimization speed

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Local search algorithms for the problem of competitive location of enterprises

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AUTOMATION AND REMOTE CONTROL 2012年第3期73卷 425-439页

作者： Beresnev, V. L. Russian Acad Sci Sobolev Inst Math Siberian Branch Novosibirsk Russia

This article deals with the mathematical model that generalizes the known problem of location of enterprises and is represented in the form of the problem of bilevel mathematical programming. In this model two competitive sides sequentially locate enterprises, and each of the sides strives to maximize its profit. As optimal solutions of the investigated problem, optimal cooperative and optimal noncooperative solutions are considered. The method is suggested for calculating the upper bounds of values of the goal function of the problem at optimal cooperative and noncooperative solutions. Simultaneously with the calculation of the upper bound, the initial approximate solution is set up. algorithms of the local search for improving this solution are suggested. The algorithms involve two stages: at the first stage the locally optimal solution is found, while at the second stage the locally optimal solution relative to the neighborhood called the generalized one is found. The results of computational experiments demonstrating the possibilities of the suggested algorithms are displayed.

关键词： search algorithms COMPUTER algorithms INDUSTRIAL location MATHEMATICAL models MATHEMATICAL programming NUMERICAL calculations

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An Educational System for Learning search algorithms and Automatically Assessing Student Performance

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2017年第1期27卷 207-240页

作者： Grivokostopoulou, Foteini Perikos, Isidoros Hatzilygeroudis, Ioannis Univ Patras Sch Engn Dept Comp Engn & Informat Patras 26504 Hellas Greece

In this paper, first we present an educational system that assists students in learning and tutors in teaching search algorithms, an artificial intelligence topic. Learning is achieved through a wide range of learning activities. Algorithm visualizations demonstrate the operational functionality of algorithms according to the principles of active learning. So, a visualization process can stop and request from a student to specify the next step or explain the way that a decision was made by the algorithm. Similarly, interactive exercises assist students in learning to apply algorithms in a step-by-step interactive way. Students can apply an algorithm to an example case, specifying the algorithm's steps interactively, with the system's guidance and help, when necessary. Next, we present assessment approaches integrated in the system that aim to assist tutors in assessing the performance of students, reduce their marking task workload and provide immediate and meaningful feedback to students. Automatic assessment is achieved in four stages, which constitute a general assessment framework. First, the system calculates the similarity between the student's and the correct answer using the edit distance metric. In the next stage, it identifies the type of the answer, based on an introduced answer categorization scheme related to completeness and accuracy of an answer, taking into account student carelessness too. Afterwards, the types of errors are identified, based on an introduced error categorization scheme. Finally, answer is automatically marked via an automated marker, based on its type, the edit distance and the type of errors made. To assess the learning effectiveness of the system an extended evaluation study was conducted in real class conditions. The experiment showed very encouraging results. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against expert (human) tutors. A total of 400 students' answers wer

关键词： Artificial intelligence curriculum search algorithms Automated assessment Intelligent tutoring system Algorithm visualization

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Mathematical Models and search algorithms for the Capacitated p-Center Problem

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INFORMS JOURNAL ON COMPUTING 2020年第2期32卷 444-460页

作者： Kramer, Raphael Iori, Manuel Vidal, Thibaut Univ Fed Pernambuco Dept Engn Prod BR-50740550 Recife PE Brazil Univ Modena & Reggio Emilia Dipartimento Sci & Metodi Ingn I-42122 Reggio Emilia Italy Pontificia Univ Catolica Rio de Janeiro Dept Informat BR-22451900 Rio De Janeiro Brazil

The capacitated p-center problem requires one to select p facilities from a set of candidates to service a number of customers, subject to facility capacity constraints, with the aim of minimizing the maximum distance between a customer and its associated facility. The problem is well known in the field of facility location, because of the many applications that it can model. In this paper, we solve it by means of search algorithms that iteratively seek the optimal distance by solving tailored subproblems. We present different mathematical formulations for the subproblems and improve them by means of several valid inequalities, including an effective one based on a 0-1 disjunction and the solution of subset sum problems. We also develop an alternative search strategy that finds a balance between traditional sequential search and binary search. This strategy limits the number of feasible subproblems to be solved and, at the same time, avoids large overestimates of the solution value, which are detrimental for the search. We evaluate the proposed techniques by means of extensive computational experiments on benchmark instances from the literature and new larger test sets. All instances from the literature with up to 402 vertices and integer distances are solved to proven optimality, including 13 open cases, and feasible solutions are found in 1,0 minutes for instances with up to 3,038 vertices.

关键词： capacitated p-center mathematical models valid inequalities search algorithms

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Computing Gap Free Pareto Front Approximations with Stochastic search algorithms

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EVOLUTIONARY COMPUTATION 2010年第1期18卷 65-96页

作者： Schuetze, Oliver Laumanns, Marco Tantar, Emilia Coello Coello, Carlos A. Talbi, El-Ghazali CINVESTAV IPN Dept Computac Mexico City 07360 DF Mexico ETH Inst Operat Res CH-8092 Zurich Switzerland INRIA Bordeaux Sud Ouest Inst Math Bordeaux F-33405 Talence France INRIA Lille Nord Europe F-59650 Villeneuve Dascq France

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was oil obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of (sic)-dominance. Though bounds on the quality of the limit approximition-which are entirely determined by the archiving strategy and the value of (sic)-have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that point f in the Pareto front can exist such that the distance of f to any image point F(a), a is an element of A, is "large." Since such gap free approximations are desirable in certain applications, and the related archiving strategies call be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy-multi-objective continuation methods-by showing that the concept of (sic)-dominance can be integrated into this approach in a suitable way.

关键词： PARETO analysis search algorithms STOCHASTIC processes APPROXIMATION theory algorithms

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Optimization of Transferable Site-Site Potentials Using a Combination of Stochastic and Gradient search algorithms

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INDUSTRIAL & ENGINEERING CHEMISTRY REsearch 2012年第17期51卷 6219-6231页

作者： Ucyigitler, Sinan Camurdan, Mehmet C. Elliott, J. Richard Univ Akron Chem & Biomol Engn Dept Akron OH 44325 USA Bogazici Univ Dept Chem Engn Istanbul Turkey

Discontinuous molecular dynamics (DMD) simulation and thermodynamic perturbation theory (TPT) have been used to study thermodynamic properties for organic compounds. The aim is to infer transferable intermolecular potential models based on correlating the vapor pressure and liquid density. The combination of DMD/TPT generates a straightforward global optimization problem for the attractive potential, instead of facing an iterative optimization simulation type problem. This global optimization problem is then formulated as a black-box optimization problem and solved using a combination of random recursive search (RRS) and Levenberg-Marquardt (LM) optimization. RRS is suitable for black-box optimization problems since its algorithm is robust to the effect of random noises in the objective function and is advantageous in optimizing the objective function with negligible parameters. LM is efficient local to an optimum with a smooth response surface. The local response surface is shown to be smooth but very flat along valleys with a high degree of coupling between the potential parameters. The algorithm is demonstrated with discretized versions of the Lennard-Jones (LJ) potential and a linear step potential using a database of 231 hydrocarbons, alcohols, aldehydes, amines, amides, esters, ethers, ketones, phenols, sulfides, and thiols. A correspondence is established between the discretized LJ potential and the TraPPE model, demonstrating the manner of improving density estimates and a way of expediting improvement of continuous transferable potentials.

关键词： MOLECULAR dynamics DISCONTINUOUS functions STOCHASTIC analysis search algorithms THERMODYNAMICS ORGANIC compounds PERTURBATION (Mathematics)

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An Educational Game for Teaching search algorithms 8

An Educational Game for Teaching Search Algorithms

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8th International Conference on Computer Supported Education (CSEDU)

作者： Grivokostopoulou, Foteini Perikos, Isidoros Hatzilygeroudis, Ioannis Univ Patras Dept Comp Engn & Informat Patras Greece

ISBN: (纸本)9789897581793

search algorithms constitute an important topic in the Artificial Intelligence curriculum and are acknowledged by most tutors to be a hard and complex domain for teachers to teach and students to deeply understand. In this paper, we present an educational computer game, designed to teach search algorithms, based on the popular Pacman game. The purpose of the educational Pacman game is to assist students to understand the artificial intelligence topic of search algorithms in an entertaining, interactive and motivating way. During their experience with the game, students can examine the behaviour of various search algorithms and a graphical annotated depiction of them through suitable visualizations. Visualizations can demonstrate the operational functionality of algorithms and are designed in line with the principles of student's active learning. Various learning activities were designed and request students to apply specific search algorithms in various example cases with or without the assistance and feedback of the game. An evaluation study was conducted in real classroom conditions and revealed quite satisfactory results. The results indicate that the educational Pacman game is an effective way to enhance students' engagement and help them to deeper understand the AI search algorithms.

关键词： Educational Game Gabe-based Learning search algorithms Algorithm Visualization Pacman Game Learning Efficiency

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