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检索条件"主题词=Multi-objective Evolutionary Algorithms"

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Many-objective Optimization Using Taxi-Cab Surface evolutionary Algorithm

Many-Objective Optimization Using Taxi-Cab Surface Evolution...

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7th International Conference on evolutionary multi-Criterion Optimization (EMO)

作者： Moen, Hans J. F. Hansen, Nikolai B. Hovland, Harald Torresen, Jim Norwegian Def Res Estab Informat Management Div Oslo Norway Univ Oslo Dept Informat N-0316 Oslo Norway Univ Oslo Dept Math N-0316 Oslo Norway

ISBN: (纸本)9783642371400

Optimization of problems spanning more than three objectives, called many-objective optimization, is often hard to achieve using modern algorithm design and currently available computational resources. In this paper a multi-objective evolutionary algorithm, called the Surface evolutionary Algorithm, is extended into many-objective optimization by utilizing, for the first time, the taxi-cab metric in the optimizer. The Surface evolutionary Algorithm offers an alternative to multi-objective optimizers that rely on the principles of domination, hypervolume and so forth. The taxi-cab metric, or Manhattan distance, is introduced as the selection criterion and the basis for calculating attraction points in the Surface evolutionary Algorithm. This allows for fast and efficient many-objective optimization previously not attainable using this method. The Taxi-Cab Surface evolutionary Algorithm is evaluated on a set of well-known many-objective benchmark test problems. In problems of up to 20 dimensions, this new algorithm of low complexity is tested against several modern multi-objective evolutionary algorithms. The results reveal the Taxi-Cab Surface evolutionary Algorithm as a conceptually simple, yet highly efficient many-objective optimizer.

关键词： multi-objective optimization multi-objective evolutionary algorithms many-objective optimization

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A New evolutionary Algorithm for Extracting a Reduced Set of Interesting Association Rules 1

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22nd International Conference on Neural Information Processing (ICONIP)

作者： Kabir, Mir Md. Jahangir Xu, Shuxiang Kang, Byeong Ho Zhao, Zongyuan Univ Tasmania Sch Engn Hobart Tas Australia Univ Tasmania ICT Hobart Tas Australia

ISBN: (数字)9783319265353

ISBN: (纸本)9783319265353;9783319265346

Data mining techniques involve extracting useful, novel and interesting patterns from large data sets. Traditional association rule mining algorithms generate a huge number of unnecessary rules because of using support and confidence values as a constraint for measuring the quality of generated rules. Recently, several studies defined the process of extracting association rules as a multi-objective problem allowing researchers to optimize different measures that can present in different degrees depending on the data sets used. Applying evolutionary algorithms to noisy data of a large data set, is especially useful for automatic data processing and discovering meaningful and significant association rules. From the beginning of the last decade, multi-objective evolutionary algorithms are gradually becoming more and more useful in data mining research areas. In this paper, we propose a new multi-objective evolutionary algorithm, MBAREA, for mining useful Boolean association rules with low computational cost. To accomplish this our proposed method extends a recent multi-objective evolutionary algorithm based on a decomposition technique to perform evolutionary learning of a fitness value of each rule, while introducing a best population and a class based mutation method to store all the best rules obtained at some point of intermediate generation of a population and improving the diversity of the obtained rules. Moreover, this approach maximizes two objectives such as performance and interestingness for getting rules which are useful, easy to understand and interesting. This proposed algorithm is applied to different real world data sets to demonstrate the effectiveness of the proposed approach and the result is compared with existing evolutionary algorithm based approaches.

关键词： Data mining Association rules multi-objective evolutionary algorithms Conditional probability Interestingness

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Bi-MOCK: A multi-objective evolutionary Algorithm for Bi-clustering with Automatic Determination of the Number of Bi-clusters 24th

Bi-MOCK: A Multi-objective Evolutionary Algorithm for Bi-clu...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Bousselmi, Meriem Bechikh, Slim Hung, Chih-Cheng Ben Said, Lamjed Univ Tunis Comp Sci Dept SMART Lab Tunis Tunisia Kennesaw State Univ Marietta GA USA Anyang Normal Univ Anyang Peoples R China

ISBN: (纸本)9783319700939;9783319700922

Bi-clustering is one of the main tasks in data mining with many possible applications in bioinformatics, pattern recognition, text mining, just to cite a few. It refers to simultaneously partitioning a data matrix based on both rows and columns. One of the main issues in bi-clustering is the difficulty to find the number of bi-clusters, which is usually pre-specified by the human user. During the last decade, a new algorithm, called MOCK, has appeared and shown its performance in data clustering where the number of clusters is determined automatically. Motivated by the interesting results of MOCK, we propose in this paper a new algorithm, called Bi-MOCK, which could be seen as an extension of MOCK for bi-clustering. Like MOCK, Bi-MOCK uses the concept of multi-objective optimization and is able to find automatically the number of bi-clusters thanks to a newly proposed variable string length encoding scheme. The performance of our proposed algorithm is assessed on a set of real gene expression datasets. The comparative experiments show the merits and the outperformance of Bi-MOCK with respect to some existing recent works.

关键词： Bi-clustering Number of bi-clusters multi-objective evolutionary algorithms Variable-size encoding

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Clustering Based Parallel Many-objective evolutionary algorithms Using the Shape of the objective Vectors 8

Clustering Based Parallel Many-Objective Evolutionary Algori...

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8th International Conference on evolutionary multi-Criterion Optimization (EMO)

作者： von Luecken, Christian Brizuela, Carlos Baran, Benjamin Univ Nacl Asuncion Fac Politecn San Lorenzo Paraguay CICESE Res Ctr Ensenada Baja California Mexico

ISBN: (纸本)9783319158921;9783319158914

multi-objective evolutionary algorithms (MOEA) are used to solve complex multi-objective problems. As the number of objectives increases, Pareto-based MOEAs are unable to maintain the same effectiveness showed for two or three objectives. Therefore, as a way to ameliorate this performance degradation several authors proposed preference-based methods as an alternative to Pareto based approaches. On the other hand, parallelization has shown to be useful in evolutionary optimizations. A central aspect for the parallelization of evolutionary algorithms is the population partitioning approach. Thus, this paper presents a new parallelization approach based on clustering by the shape of objective vectors to deal with many-objective problems. The proposed method was compared with random and k-means clustering approaches using a multi-threading framework in parallelization of the NSGA-II and six variants using preference-based relations for fitness assignment. Executions were carried-out for the DTLZ problem suite, and the obtained solutions were compared using the generational distance metric. Experimental results show that the proposed shape-based partition achieves competitive results when comparing to the sequential and to other partitioning approaches.

关键词： multi-objective evolutionary algorithms Many-objective optimization Parallel evolutionary algorithms

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Methodologies for Solving Complex multi-objective Combinatorial Problems in Engineering: An evolutionary Approach

Methodologies for Solving Complex Multi-Objective Combinator...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Donoso, Yezid Univ Los Andes Syst & Comp Engn Dept Master Program Informat Secur Bogota Colombia

ISBN: (纸本)9781509011476

In real problems in Engineering, solving a problem is not enough;the solution of the problem must be the best solution possible. In other words, it is necessary to find the optimal solution. The solution is the best possible solution because in the real world this problem may have certain constraints by which the solutions found may be feasible, that is, they can be implemented in practice and, unfeasible or that they cannot be implemented. Some of these problems in engineering can be MOP (multi-objective Optimization Problem). A general MOP includes a set of n parameters ( decision variables), a set of k objective functions and a set of m restrictions. The objective and restriction functions are functions of the decision variables where is possible to obtain a set of optimal values. Then the MOP can be expressed as: Optimize y = f(x) = (fl( x), f2(x),..., fk(x)) Subject to e(x) = (el(x), e2(x),..., em(x)) 0 Where x = (x1, x2,..., xn) X y = (y1, y2,..., yk) Y The method evolutionary algorithm (EA) refers to searching and optimization techniques based on the evolution model proposed by Charles Darwin. Genetic algorithms are used in several areas especially for searching and optimizations. In the real case the algorithm is implemented by choosing a coding for the possible solutions to the problem. The coding is done through chains of bits, numbers or characters that represent the chromosomes. The crossing and mutation operations are applied in a very simple way through functions of vector value manipulation. The EAs are interesting given the fact that at first glance they seem especially apt to deal with the difficulties presented by MOPs. The reason for this is that they can return an entire set of solutions after a simple run and they do not have any other of the limitations of traditional techniques. In addition, some researchers have suggested that the EAs would behave better than other blind searching techniques.

关键词： Complex Problems evolutionary algorithms multi-objective Optimization Problems multi-objective evolutionary algorithms

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multi-objective evolutionary Neural Network to Predict Graduation Success at the United States Military Academy

Multi-objective Evolutionary Neural Network to Predict Gradu...

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Complex Adaptive Systems Conference (CAS) - Cyber Physical Systems and Deep Learning

作者： Lesinski, Gene Corns, Steven US Mil Acad West Point NY 10996 USA Missouri Univ Sci & Technol Rolla MO 65409 USA

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： evolutionary algorithms Neural network multi-objective evolutionary algorithms enrollment management student retention

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Exploring the Pareto frontier using multi-sexual evolutionary algorithms: an application to a flexible manufacturing problem

Exploring the Pareto frontier using multi-sexual evolutionar...

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Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary Computation V

作者： Bonissone, S Subbu, R GE Co Global Res Ctr Schenectady NY 12309 USA

ISBN: (纸本)0819445541

In multi-objective optimization (MOO) problems we need to optimize or at least satisfy many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionary algorithms (EAs) to solve these problems. EAs are computer programs that generate solutions by simulating a Darwinian evolution. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score.. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.

关键词： multi-objective evolutionary algorithms multi-sexual evolutionary algorithms Pareto frontier flexible manufacturing

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multi-objective evolutionary fuzzy modeling for the docking maneuver of an automated guided vehicle

Multi-objective evolutionary fuzzy modeling for the docking ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lucas, JM Martínez-Barberá, H Jiménez, F Univ Murcia Dept Informat & Commun Engn Murcia Spain

ISBN: (纸本)0780392981

In many real-world applications, mobile robots require interacting with objects in their environments by means of performing docking tasks in a precise manner. In the application domain of this work, an Automated Guided Vehicle (AGV), specifically, a fork-lift truck must often perform docking maneuvers to load pallets in conveyor belts. The main purpose is to improve some features of docking task as its duration, accuracy and stability. We propose a soft computing technique based on a multiobjective evolutionary algorithm in order to find multiples fuzzy logic controllers which optimize specific objectives and satisfy imposed constraints for docking task in charge of following up an online generated trajectory.

关键词： multi-objective evolutionary algorithms path following path tracking mobile robots constraint optimization fuzzy control

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A Classification Surrogate Model based evolutionary Algorithm for Neural Network Structure Learning

A Classification Surrogate Model based Evolutionary Algorith...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Hu, Wenyue Zhou, Aimin Zhang, Guixu East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781728169262

Designing neural networks often requires a large number of artificial intelligence experts. However, such manual processes are time-consuming and require numerous resources. In this paper, we try to search neural network structures automatically for the image classification task. Moreover, considering the huge computational cost of neural architecture search (NAS), we attempt to apply a classification surrogate model based multi-objective evolutionary algorithm to search neural network architectures (CSMEA-Net). The algorithm combines two objectives, i.e., minimizing the validation error and the computational complexity measured by the number of floating-point operations (FLOPs) to achieve Pareto Optimality. In addition, we improve the components of the cell-based search space. The performance of network architectures discovered by our method is evaluated on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed approach can find a higher-performance neural network architecture compared with both hand-crafted as well as automatically-designed networks.

关键词： convolutional neural network neural architecture search multi-objective evolutionary algorithms image classification

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Parallel Dynamic multi-objective Optimization evolutionary Algorithm 22

Parallel Dynamic Multi-Objective Optimization Evolutionary A...

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22nd International Arab Conference on Information Technology (ACIT)

作者： Grid, Maroua Belaiche, Leila Kahloul, Laid Benharzallah, Saber Biskra Univ Comp Sci Dept LINFI Lab Biskra Algeria Batna 2 Univ Batna Algeria

ISBN: (纸本)9781665419956

multi-objective optimization evolutionary algorithms (MOEAs) are considered as the most suitable heuristic methods for solving multi-objective optimization problems (MOPs). These MOEAs aim to search for a uniformly distributed, near-optimal and near-complete Pareto front for a given MOP. However, MOEAs fail to achieve their aim completely because of their fixed population size. To overcome this limit, an evolutionary approach of multi-objective optimization was proposed;the dynamic multi-objective evolutionary algorithms (DMOEAs). This paper deals with improving the user requirements (i.e., getting a set of optimal solutions in minimum computational time). Although, DMOEA has the distinction of dynamic population size, being an evolutionary algorithm means that it will certainly be characterized by long execution time. One of the main reasons for adapting parallel evolutionary algorithms (PEAs) is to obtain efficient results with an execution time much lower than the one of their sequential counterparts in order to tackle more complex problems. Thus, we propose a parallel version of DMOEA (i.e., PDMOEA). As experimental results, the proposed PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimization of computational time and converging to the desired population size.

关键词： multi-objective problems Pareto front multi-objective evolutionary algorithms Dynamic MOEA Parallel DMOEA

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