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检索条件"主题词=Multiobjective Evolutionary Algorithms"

共 145 条记录，以下是71-80 订阅

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A Parallel multiobjective Approach to Evolving Cellular Automata Rules by Cell State Change Dynamics

A Parallel Multiobjective Approach to Evolving Cellular Auto...

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1st BRICS Countries Congress on Computational Intelligence / 11th Brazilian Congress on Computational Intelligence (BRICS-CCI and CBIC)

作者： Iclanzan, David Chira, Camelia Univ Babes Bolyai Dept Comp Sci Kogalniceanu 1 Cluj Napoca 400084 Romania Univ Babes Bolyai Dept Comp Sci Ctr Study Complexity Cluj Napoca 400327 Romania

ISBN: (纸本)9781479931941

The complex regimes of operation situated between ordered and chaotic behavior are hypothesized to give rise to computational capabilities. Lacking an universal blueprint for the emergence of complexity, a costly search is typically used to find the configurations of distributed artificial systems that can facilitate global computation. In this paper, we address the tedious task of searching for complex cellular automata rules able to lead to a certain global behavior based on local interactions. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. A classical heuristic search guided only by a coarse approximation of the ability of a rule to perform in certain conditions will generally not reach beyond an ordered regime of operation. To overcome this limitation, in this paper we incorporate a promising heuristic that rewards increased dynamics with regard to cell state changes in a multiobjective, parallel evolutionary framework. The scope of the multiobjective formulation is to balance the search between ordered and chaotic regimes in order to facilitate the discovery of rules exhibiting complex behaviors. Experimental results confirm that the combined approach represents an efficient way for supporting the emergence of complexity as in all runs we were able to find cellular automata exhibiting a high degree of global self-organization.

关键词： multiobjective evolutionary algorithms Parallelization on Graphics Processing Units Cellular Automata Density Classification Task

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MOEA/VAN: multiobjective evolutionary Algorithm Based on Vector Angle Neighborhood 15

MOEA/VAN: Multiobjective Evolutionary Algorithm Based on Vec...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Denysiuk, Roman Costa, Lino Santo, Isabel Espirito Univ Minho Algoritmi R&D Ctr P-4710057 Braga Portugal Univ Minho Dept Prod & Syst Engn P-4710057 Braga Portugal

ISBN: (纸本)9781450334723

Natural selection favors the survival and reproduction of organisms that are best adapted to their environment. Selection mechanism in evolutionary algorithms mimics this process, aiming to create environmental conditions in which artificial organisms could evolve solving the problem at hand. This paper proposes a new selection scheme for evolutionary multiobjective optimization. The similarity measure that defines the concept of the neighborhood is a key feature of the proposed selection. Contrary to commonly used approaches, usually defined on the basis of distances between either individuals or weight vectors, it is suggested to consider the similarity and neighborhood based on the angle between individuals in the objective space. The smaller the angle, the more similar individuals. This notion is exploited during the mating and environmental selections. The convergence is ensured by minimizing distances from individuals to a reference point, whereas the diversity is preserved by maximizing angles between neighboring individuals. Experimental results reveal a highly competitive performance and useful characteristics of the proposed selection. Its strong diversity preserving ability allows to produce a significantly better performance on some problems when compared with stat-of-the-art algorithms.

关键词： multiobjective optimization evolutionary multiobjective optimization multiobjective evolutionary algorithms Performance assessment

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Ensemble-Based Constraint Handling in multiobjective Optimization 19

Ensemble-Based Constraint Handling in Multiobjective Optimiz...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Vodopija, Aljosa Oyama, Akira Filipic, Bogdan Joef Stefan Inst Ljubljana Slovenia Joef Stefan Int Postgrad Sch Ljubljana Slovenia Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa Japan

ISBN: (纸本)9781450367486

Many real-world optimization problems involve both multiple objectives and constraints. Although constraint handling in multiobjective optimization has been considered in the literature, there is still a high demand for more advanced and versatile constraint handling techniques (CHTs) in real-world applications. For this reason, we propose a general approach to combine multiple CHTs into an ensemble-based method, providing a framework to easily construct new CHTs from existing ones. The approach is evaluated on nine test problems from the literature using an ensemble of four widely-used CHTs. The experimental results show that the ensemble is more robust than single CHTs and performs at least as well as the best single CHTs on all the test problems. Moreover, a positive synergistic effect of the ensemble is demonstrated on three problems.

关键词： constrained multiobjective optimization multiobjective evolutionary algorithms constraint handling techniques ensemble-based methods NSGA-II

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A Memetic Algorithm with One-step Local Search to Guide Diversity Increase in Dynamic multiobjective Problems

A Memetic Algorithm with One-step Local Search to Guide Dive...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Garrozi, Cicero Araujo, Aluizio F. R. Univ Pernambuco Recife PE Brazil Univ Fed Pernambuco Recife PE Brazil Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9781457706530

The multiobjective evolutionary algorithms (MOEAs) are often applied to solve difficult optimization problems, but the dynamic case is even more special. During the optimization, if the environment is changed, a dynamic algorithm must temporarily increase the exploration and decrease the exploitation to generate genetic diversity and then be capable of handling the new behavior of the environment. A technique to increase the diversity may impose an extra delay to such an algorithm that needs to be fast because the new changes may arrive at any time. This paper proposes a model that adds a mutation operator based on gradient, which has the purpose of generating guided diversity to respond to changes in the environment, hence it can accelerate the convergence of the algorithm as a whole. The memetic mutation operator was inserted in the SPEA2 to respond more efficiently to the modifications. Simulations of the proposed model (called Gradient Guided SPEA2, GSPEA2) were carried out for the benchmarks FDA1, FDA3, and DIMP1. Considering the metrics VDweighted and MSweighted, performance of SPEA2 with GSPEA2 was compared with other four dynamic MOEAs. Results suggest that this is a promising approach.

关键词： Convergence evolutionary computation Heuristic algorithms MOEA Measurement Memetics Optimization SPEA2 Simulation dynamic multiobjective problems evolutionary computation memetic algorithm multiobjective evolutionary algorithms one-step local search optim

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Topology and Sizing Optimization of Trusses with Adaptive Ground Finite Elements Using multiobjective PBIL

Topology and Sizing Optimization of Trusses with Adaptive Gr...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： Noilublao, Chaid Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

ISBN: (纸本)9783037852132

This paper presents an integrated design technique to carry out simultaneous topology and sizing optimization of a two-dimensional truss structure. An optimization problem is set to find a structural layout and elements' cross-sectional areas of a 2D truss such that objective functions including mass, compliance, and buckling factor are optimized. Design constraints consist of stress, buckling, and compliance. The concept of an adaptive ground elements approach and the encoding/decoding process are detailed. The multiobjective version of population-based incremental learning (PBIL) is employed to solve the design problem. The results reveal that the proposed design strategy is efficient and can be an effective engineering design tool.

关键词： Topology optimization truss structure multiobjective evolutionary algorithms finite element analysis

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multiobjective evolutionary Optimization of Quadratic Takagi-Sugeno Fuzzy Rules for Remote Bathymetry Estimation

Multiobjective Evolutionary Optimization of Quadratic Takagi...

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Oceans 2015 Genova

作者： Cococcioni, Marco Lazzerini, Beatrice Univ Pisa Dept Informat Engn I-56122 Pisa Italy

ISBN: (纸本)9781479987375

In this work we tackle the problem of bathymetry estimation using: i) a multispectral optical image of the region of interest, and ii) a set of in situ measurements. The idea is to learn the relation that between the reflectances and the depth using a supervised learning approach. In particular, quadratic Takagi-Sugeno fuzzy rules are used to model this relation. The rule base is optimized by means of a multiobjective evolutionary algorithm. To the best of our knowledge this work represents the first use of a quadratic Takagi-Sugeno fuzzy system optimized by a multiobjective evolutionary algorithm with bounded complexity, i. e., able to control the complexity of the consequent part of second-order fuzzy rules. This model has an outstanding modeling power, without inheriting the drawback of complexity due to the use of quadratic functions ( which have complexity that scales quadratically with the number of inputs). This opens the way to the use of the proposed approach even for medium/ high dimensional problems, like in the case of hyper-spectral images.

关键词： Bathymetry estimation quadratic Takagi-Sugeno fuzzy rules remotely sensed optical images multiobjective evolutionary algorithms

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Using multiobjective metaheuristics to solve VRP with uncertain demands

Using Multiobjective metaheuristics to solve VRP with uncert...

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2010 IEEE World Congress on Computational Intelligence

作者： Sulieman, Dalia Jourdan, Laetitia Talbi, El-Ghazali Univ Lille 1 INRIA Lille Nord Europe LIFL F-59655 Villeneuve Dascq France Univ Lille 3 INRIA Lille Nord Europe F-59650 Villeneuve Dascq France Univ Lille 3 INRIA Lille Nord Europe LIFL F-59650 Villeneuve Dascq France

ISBN: (纸本)9781424481262

In real life optimization problems, it is very important to have high quality solutions (optimal). But when uncertainty becomes part of the optimization problem, solutions should be optimal and robust to the uncertain environmental changes. This paper focuses on finding robust optimal solution for the vehicle routing problem with stochastic demands VRPSD. In this case when the uncertainty of the customers demands enters this problem, the classical methods of VRP can not be used to obtain optimal solutions. We need new methods with new strategies to have robust optimal solution. For that we propose two bi-objective models, depending on the multi-objective evolutionary algorithms MOEAs: IBEA, MOGA and NSGAII. We compare the robustness degree of the two models and also we compare the performance of the three MOEAs over these two models.

关键词： IBEA MOEA MOGA NSGAII VRPSD customer satisfaction customers demands evolutionary computation goods distribution multiobjective evolutionary algorithms multiobjective metaheuristics optimisation real life optimization problems robust optimal solution stochastic demands stochastic processes transportation uncertain demands uncertain environmental changes vehicle routing problem

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multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm

Multiobjective optimization of radio-to-fiber repeater place...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chan, T. M. Man, K. F. Tang, K. S. Kwong, S. City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)0780394836

This paper considers the radio-to-fiber repeater placement problem in Wireless Local Loop (WLL) Systems. The severe problem that the WLL systems encountered is that the large diffraction loss from rooftop to street occurs at its frequency band, 2.3 GHz. The radio-to-fiber repeaters can be used for the remedy of this situation. Unlike the conventional WLL systems, the total system cost of this option depends on the additional repeaters and optical fibers (links). Thus, our objective is to minimize the total repeater cost and total link cost simultaneously by selecting optimal locations for the repeaters. It is a multiobjective problem in which a tradeoff between the total repeater cost and total link cost can thus be made. A new jumping gene paradigm called Jumping-Gene Genetic Algorithm (JGGA) is proposed to solve this conflicting dilemma. The main feature of JGGA is that it only consists of a simple operation in which a transposition of the gene(s) is induced within the same or another chromosome within the framework of Genetic Algorithm. The algorithm has been tested by using two specific performance metrics in evaluating the quality of obtained sets of non-dominated solutions. Simulation results revealed from this study that JGGA is able to find non-dominated solutions with better convergence and diversity than other multiobjective evolutionary algorithms.

关键词： wireless local loop radio-to-fiber repeater placement multiobjective evolutionary algorithms optimization genetic algorithms jumping genes

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multiobjective optimization of ensembles of multilayer perceptrons for pattern classification

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9th International Conference on Parallel Problem Solving from Nature (PPSN IX)

作者： Castillo, P. A. Arenas, M. G. Merelo, J. J. Rivas, V. M. Romero, G. Univ Granada Dept Architecture & Comp Technol E-18071 Granada Spain Univ Jaen Dept Comp Sci Jaen Spain

ISBN: (纸本)3540389903

Pattern classification seeks to minimize error of unknown patterns, however, in many real world applications, type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. Actually, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. Despite the importance of type-II errors, most pattern classification methods take into account only the global classification error. In this paper we propose to optimize both error types in classification by means of a multiobjective algorithm in which each error type and the network size is an objective of the fitness function. A modified version of the GProp method (optimization and design of multilayer perceptrons) is used, to simultaneously optimize the network size and the type I and II errors.

关键词： multiobjective optimization multiobjective evolutionary algorithms Pareto optimality artificial neural networks optimization gprop pattern classification

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Improving a multiobjective evolutionary algorithm applied to batch scheduling in pharmaceutical manufacturing 35

Improving a multiobjective evolutionary algorithm applied to...

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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Kohara, Debora Toshie Barbosa de Oliveira, Gina Maira Almeida Martins, Luiz Gustavo Univ Fed Uberlandia Fac Comp Uberlandia Brazil

ISBN: (纸本)9798350342734

multiobjective evolutionary algorithms (MOEA's) have been developed for optimization problems involving conflicting objectives. Real-world Batch Sequencing (BS) in pharmaceutical manufacturing presents a multiobjective optimization challenge, compounded by constraints and uncertain demands. This complexity often leads to low convergence of feasible solutions. In this study, we evaluate population initialization strategies, propose an optimized mutation operator, and explore various crossover types to enhance solution quality, measured by metrics including the number of non-dominated feasible solutions (NFS), hypervolume (HV), Inverted Generational Distance Plus (IGD+), Error Rate (E), and Coverage of Two Sets (CS).

关键词： multiobjective evolutionary algorithms Constrained multiobjective Optimization Pharma Manufacturing

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