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An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

Computers, Materials & Continua 2023年第2期74卷 4119-4133页

作者： Hasanien K.Kuba Mustafa A.Azzawi Saad M.Darwish Oday A.Hassen Ansam A.Abdulhussein University of Information Technology and Communication(UOITC) Baghdad10081Iraq Accenture Ling Karan TRXKuala Lumpur55188Malaysia Department of Information Technology Institute of Graduate Studies and ResearchUniversity of Alexandria21526Egypt Ministry of Education Wasit Education DirectorateKut52001Iraq Information Technology Center Iraqi Commission for Computers and InformaticsBaghdad10081Iraq

It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to tackle.In this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.

关键词： Distributed data mining evolutionary computation sanitization process healthcare informatics

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Solving multi-scenario cardinality constrained optimization problems via multi-objective evolutionary algorithms

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Science China(Information Sciences) 2019年第9期62卷 73-90页

作者： Xing ZHOU Huaimin WANG Wei PENG Bo DING Rui WANG College of Computer National University of Defense Technology College of Systems Engineering National University of Defense Technology

Cardinality constrained optimization problems(CCOPs) are fixed-size subset selection problems with applications in several fields. CCOPs comprising multiple scenarios, such as cardinality values that form an interval, can be defined as multi-scenario CCOPs(MSCCOPs). An MSCCOP is expected to optimize the objective value of each cardinality to support decision-making processes. When the computation is conducted using traditional optimization algorithms, an MSCCOP often requires several passes(algorithmic runs) to obtain all the(near-)optima, where each pass handles a specific cardinality. Such separate passes abandon most of the knowledge(including the potential superior solution structures) learned in one pass that can also be used to improve the results of other passes. By considering this situation, we propose a generic transformation strategy that can be referred to as the Mucard strategy, which converts an MSCCOP into a low-dimensional multi-objective optimization problem(MOP) to simultaneously obtain all the(near-)optima of the MSCCOP in a single algorithmic run. In essence, the Mucard strategy combines separate passes that deal with distinct variable spaces into a single pass, enabling knowledge reuse and knowledge interchange of each cardinality among genetic individuals. The performance of the Mucard strategy was demonstrated using two typical MSCCOPs. For a given number of evolved individuals, the Mucard strategy improved the accuracy of the obtained solutions because of the in-process knowledge than that obtained by untransformed evolutionary algorithms, while reducing the average runtime. Furthermore, the equivalence between the optimal solutions of the transformed MOP and the untransformed MSCCOP can be theoretically proved.

关键词： evolutionary computation multi-objective optimization cardinality-constrained optimization problem multiple scenarios transformation p-median problem portfolio optimization problem

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Analyzing the Simple Ranking and Selection Process for Constrained evolutionary Optimization

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Journal of Computer Science & Technology 2008年第1期23卷 19-34页

作者： Ehab Z.Elfeky Ruhul A.Sarker Daryl L.Essam School of Information Technology and Electrical Engineering University of New South Wales

Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with six other evolutionary based methods, using 12 benchmark problems from the literature, it shows very encouraging performance. T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms. A study has also been carried out in order to show the effect of each component of the proposed algorithm.

关键词： constrained continuous optimization evolutionary computation genetic algorithms multi-parent crossover

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different problems.This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Metaheuristics evolutionary computation Swarm intelligence Variation operator Transformation invariance Automated design

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User’s Attention Knowledge Learning in Interactive evolutionary computation

User’s Attention Knowledge Learning in Interactive Evolutio...

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2009中国控制与决策会议

作者： HAO Guo-Sheng~(1,2),GONG Dun-Wei~1,YUAN Jie~1,YAN Yu-Ruo~2,YAN Jun-Rong~2 1.School of Information & Electronic Engineering,China University of Mining & Technology,Xuzhou,Jiangsu 221008,China 2.School of Computer Science and Technology,Xuzhou Normal University,Xuzhou,Jiangsu,221116,China

＜正＞A user’s attention in interactive evolutionary computation（IEC） is an important issue.The methods to learn the user’s attention knowledge in IEC are studied in this paper.Firstly,the definition of the user’s attention is given. Secondly,the user’s attention on gene sense unit and some related theorems are given.Based on these theorems,the methods to learn the user’s attention knowledge are presented.Thirdly,a new method to improve the performance of IEC based on the user’s attention knowledge is given.The experiments validate the efficiency of the methods.The study on the user’s attention in IEC establishes a necessary foundation for reducing users’ fatigue in IEC.

关键词： evolutionary computation Interaction User’s Attention Knowledge Learning

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An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1290-1299页

作者： Maryam Parsajoo Ahmed Salih Mohammed Saffet Yagiz Danial Jahed Armaghani Manoj Khandelwal Department of Mining Engineering Imam Khomeini International UniversityQazvin34149-16818Iran Civil Engineering Department College of EngineeringUniversity of SulaimaniKurdistan Region46001Iraq School of Mining and Geosciences Nazarbayev UniversityNur-Sultan010000Kazakhstan Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallarat3350Australia

Field penetration index(FPI) is one of the representative key parameters to examine the tunnel boring machine(TBM) performance.Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering.This study aims to predict TBM performance(i.e.FPI) by an efficient and improved adaptive neuro-fuzzy inference system(ANFIS) model.This was done using an evolutionary algorithm,i.***.artificial bee colony(ABC) algorithm mixed with the ANFIS model.The role of ABC algorithm in this system is to find the optimum membership functions(MFs) of ANFIS model to achieve a higher degree of accuracy.The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index(BI),fracture spacing,α angle between the plane of weakness and the TBM driven direction,and field single cutter load were assigned as model inputs to approximate FPI values.According to the results obtained by performance indices,the proposed ANFISABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model.In terms of coefficient of determination(R^(2)),the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFISABC model,respectively,which confirm its power and capability in solving TBM performance problem.The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions.

关键词： Tunnel boring machine(TBM) Field penetration index(FPI) Neuro-fuzzy technique evolutionary computation Artificial bee colony(ABC)

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evolutionary Multi-objective Portfolio Optimization in Practical Context

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International Journal of Automation and computing 2008年第1期5卷 67-80页

作者： S.C.Chiam A.Al Mamum Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore

This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.

关键词： evolutionary computation multi-objective optimization portfolio optimization preference-based multi-objective optimization constraint handling

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An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem

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International Journal of Intelligent Computing and Cybernetics 2016年第1期9卷 2-22页

作者： Carin Lightner-Laws Vikas Agrawal Constance Lightner Neal Wagner Clayton State University MorrowGeorgiaUSA Jacksonville University JacksonvilleFloridaUSA Fayetteville State University FayettevilleNorth CarolinaUSA MIT Lincoln Laboratory LexingtonMassachusettsUSA

Purpose-The purpose of this paper is to explore a real world vehicle routing problem(VRP)that has multi-depot subcontractors with a heterogeneous fleet of vehicles that are available to pickup/deliver jobs with varying time windows and locations.Both the overall job completion time and number of drivers utilized are analyzed for the automated job allocations and manual job assignments from transportation field experts.Design/methodology/approach-A nested genetic algorithm(GA)is used to automate the job allocation process and minimize the overall time to deliver all jobs,while utilizing the fewest number of drivers-as a secondary objective.Findings-Three different real world data sets were used to compare the results of the GA vs transportation field experts’manual assignments.The job assignments from the GA improved the overall job completion time in 100 percent(30/30)of the cases and maintained the same or fewer drivers as BS Logistics(BSL)in 47 percent(14/30)of the cases.Originality/value-This paperprovidesa novel approach to solving a real world VRPthathasmultiple variants.While there have been numerous models to capture a select number of these variants,the value of this nested GA lies in its ability to incorporate multiple depots,a heterogeneous fleet of vehicles as well as varying pickup times,pickup locations,delivery times and delivery locations for each job into a single model.Existing research does not provide models to collectively address all of these variants.

关键词： Genetic algorithms evolutionary computation Vehicle routing Multiple depot transportation Hard/soft time windows

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Diploidy in evolutionary algorithms for dynamic optimization problems A best-chromosome-wins dominance mechanism

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International Journal of Intelligent Computing and Cybernetics 2015年第4期8卷 312-329页

作者： Boris Shabash Kay C.Wiese School of Computing Science Simon Fraser UniversityBurnabyCanada

Purpose–In this work,the authors show the performance of the proposed diploid scheme(a representation where each individual contains two genotypes)with respect to two dynamic optimization problems,while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms(EAs)to standard EAs.The paper aims to discuss this issue.Design/methodology/approach–In the proposed diploid representation of EA,each individual possesses two copies of the genotype.In order to convert this pair of genotypes to a single phenotype,each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype.In order to provide a fair and objective comparison,the authors make sure to compare populations which contain the same amount of genetic information,where the only difference is the arrangement and interpretation of the information.The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position.Findings–For small fitness landscapes the haploid(standard)and diploid algorithms perform comparably and are able to find the global optimum very quickly.However,as the search space increases,rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum.Since both algorithms use the same amount of genetic information,it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better.Originality/value–The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele(value)in the vector of values that is the genotype,dominance is adopted across the entire genotype in relation to its homologue.As a result,this representation can be extended across any alphabet,for any optimization function.

关键词： evolutionary computation Genetic algorithms Diploidy Dynamic optimization

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An evolutionary Approach to the Delineation of Functional Areas Based on Travel-to-work Flows

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International Journal of Automation and computing 2008年第1期5卷 10-21页

作者： Francisco Flórez-Revuelta JoséManuel Casado-Díaz Lucas Martínez-Bernabeu Department of Computing Technology University of AlicanteP.O.Box 99E-03080AlicanteSpain Institute of International Economics University of AlicanteP.O.Box 99F-03080AlicanteSpain

This paper presents a new approach to the delineation of local labor markets based on evolutionary computation. The aim of the exercise is the division of a given territory into functional regions based on travel-to-work flows. Such regions are defined so that a high degree of inter-regional separation and of intra-regional integration in both cases in terms of commuting flows is guaranteed. Additional requirements include the absence of overlap between delineated regions and the exhaustive coverage of the whole territory. The procedure is based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. In the experimentation stage, two variations of the fitness function are used, and the process is also applied as a final stage for the optimization of the results from one of the most successful existing methods, which are used by the British authorities for the delineation of travel-to-work areas （TTWAs）. The empirical exercise is conducted using real data for a sufficiently large territory that is considered to be representative given the density and variety of travel-to-work patterns that it embraces. The paper includes the quantitative comparison with alternative traditional methods, the assessment of the performance of the set of operators which has been specifically designed to handle the regionalization problem and the evaluation of the convergence process. The robustness of the solutions, something crucial in a research and policy-making context, is also discussed in the paper.

关键词： evolutionary computation regional science economics optimization methods

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