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检索条件"主题词=Multiobjective evolutionary algorithms"

共 145 条记录，以下是121-130 订阅

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Introducing a Robust and Efficient Stopping Criterion for MOEAs

Introducing a Robust and Efficient Stopping Criterion for MO...

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2010 IEEE World Congress on Computational Intelligence

作者： Guerrero, Jose L. Marti, Luis Berlanga, Antonio Garcia, Jesus Molina, Jose M. Univ Carlos III Madrid Grp Artificial Intelligence GIAA Dept Comp Sci Madrid 28270 Spain

ISBN: (纸本)9781424481262

Soft computing methods, and Multi-Objective evolutionary algorithms (MOEAs) in particular, lack a general convergence criterion which prevents these algorithms from detecting the generation where further evolution will provide little improvements (or none at all) over the current solution, making them waste computational resources. This paper presents the Least Squares Stopping Criterion (LSSC), an easily configurable and implementable, robust and efficient stopping criterion, based on simple statistical parameters and residue analysis, which tries to introduce as few setup parameters as possible, being them always related to the MOEAs research field rather than the techniques applied by the criterion.

关键词： MOEA convergence criterion evolutionary computation least squares approximations least squares stopping criterion multiobjective evolutionary algorithms residue analysis soft computing methods statistical analysis statistical parameters

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A Study on the Effects of Rankings Sensitive to Density on Many-objective MNK Landscapes

A Study on the Effects of Rankings Sensitive to Density on M...

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2010 IEEE World Congress on Computational Intelligence

作者： Aguirre, Hernan Tanaka, Kiyoshi Shinshu Univ Int Young Researcher Empowerment Ctr 4-17-1 Wakasato Nagano 3808553 Japan Shinshu Univ Fac Engn Nagano 3808553 Japan

ISBN: (纸本)9781424481262

This work investigates epsilon-ranking and epsilon-box non-domination sorting, two methods that incorporate epsilon-dominance concepts to estimate density of solutions and control the number of rank-1 solution for many-objective optimization. We study how convergence and spread of solutions are affected by rankings that are based on local information of the distribution of solutions without considering closeness-to-dominance information. We also study the robustness of the methods to parameters settings, and how the methods react when extreme solutions are enforced or not. MNK-Landscapes are used as test problems in our study.

关键词： box nondomination sorting closeness-to-dominance information e-dominance concepts e-ranking evolutionary computation many-objective MNK landscapes multiobjective evolutionary algorithms parameters settings

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Policy Gradient Approaches for Multi-Objective Sequential Decision Making: A Comparison

Policy Gradient Approaches for Multi-Objective Sequential De...

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IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

作者： Parisi, Simone Pirotta, Matteo Smacchia, Nicola Bascetta, Luca Restelli, Marcello Politecn Milan Dept Elect Informat & Bioengn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9781479945528

This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov Decision Processes (MOMDPs). Despite the popularity of policy-gradient algorithms and the fact that gradient-ascent algorithms have been already proposed to numerically solve multi-objective optimization problems, especially in combination with multi-objective evolutionary algorithms, so far little attention has been paid to the use of gradient information to face multi-objective sequential decision problems. Three different Multi-Objective Reinforcement-Learning (MORL) approaches are here presented. The first two, called radial and Pareto following, start from an initial policy and perform gradient-based policy-search procedures aimed at finding a set of non-dominated policies. Differently, the third approach performs a single gradient-ascent run that, at each step, generates an improved continuous approximation of the Pareto frontier. The parameters of a function that defines a manifold in the policy parameter space are updated following the gradient of some performance criterion so that the sequence of candidate solutions gets as close as possible to the Pareto front. Besides reviewing the three different approaches and discussing their main properties, we empirically compare them with other MORL algorithms on two interesting MOMDPs.

关键词： Pareto optimisation approximation theory decision making evolutionary computation gradient methods learning (artificial intelligence) MOMDPs MORL approaches Pareto following Pareto frontier approximation gradient-ascent algorithms gradient-based policy-search procedures multiobjective Markov decision processes multiobjective evolutionary algorithms multiobjective optimization problems multiobjective reinforcement-learning approaches multiobjective sequential decision making nondominated policies performance criterion policy gradient approaches policy-gradient algorithms radial following Algorithm design and analysis Approximation algorithms Approximation methods Manifolds Measurement Optimization Water resources evolutionary algorithm Performance metrics Pareto optimisation Algorithm design and analysis Manifolds Approximation method gradient methods Approximation Theory Approximation algorithms Water Resources Policies decision making

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Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection 16th

Addressing Overlapping in Classification with Imbalanced Dat...

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16th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Fernandez, Alberto Jose del Jesus, Maria Herrera, Francisco Univ Jaen Dept Comp Sci Jaen Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9783319248349;9783319248332

In classification tasks with imbalanced datasets the distribution of examples between the classes is uneven. However, it is not the imbalance itself which hinders the performance, but there are other related intrinsic data characteristics which have a significance in the final accuracy. Among all, the overlapping between the classes is possibly the most significant one for a correct discrimination between the classes. In this contribution we develop a novel proposal to deal with the former problem developing a multi-objective evolutionary algorithm that optimizes both the number of variables and instances of the problem. Feature selection will allow to simplify the overlapping areas easing the generation of rules to distinguish between the classes, whereas instance selection of samples from both classes will address the imbalance itself by finding the most appropriate class distribution for the learning task, as well as removing noise and difficult borderline examples. Our experimental results, carried out using C4.5 decision tree as baseline classifier, show that this approach is very promising. Our proposal outperforms, with statistical differences, the results obtained with the SMOTE+ENN oversampling technique, which was shown to be a baseline methodology for classification with imbalanced datasets.

关键词： Imbalanced classification Overlapping Feature selection Instance selection multiobjective evolutionary algorithms

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Reducing energy use and operational cost of air conditioning systems with multi-objective evolutionary algorithms

Reducing energy use and operational cost of air conditioning...

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2010 IEEE World Congress on Computational Intelligence

作者： Perfumo, Cristian Ward, John K. Braslavsky, Julio H. CSIRO Clayton Vic Australia Univ Newcastle Callaghan NSW 2308 Australia Univ Newcastle Sch Elect Engn & Comp Sci ARC Cte Excellence Complex Dynam Syst & Control Callaghan NSW 2308 Australia

ISBN: (纸本)9781424481262

Air conditioning is responsible for around 60% of energy use in commercial buildings and is rapidly increasing in the residential sector. Although each system is individually small, the proliferation of air conditioning and the correlation of energy use with temperature is driving peak demand and the need for electricity distribution network upgrades. Energy retailers are now looking for ways to reduce this aggregate peak demand, leading to a tradeoff between peak demand, energy cost and the thermal comfort of building occupants. This paper presents a multi-objective evolutionary algorithm (MOEA) to quantify trade-offs amongst these three competing goals. We study a scenario with 8 air conditioners (ACs) and compare our findings against the case of having all ACs working independently, irrespective of global goals. The results show that, with statistically significant certainty, any run of the MOEA outperforms any scenario where the ACs function independently to keep a given level of comfort on a typical hot day.

关键词： HVAC air conditioning systems commercial buildings costing electricity distribution network upgrades energy use evolutionary computation multiobjective evolutionary algorithms operational cost residential sector

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A Phenomic Approach to Genetic algorithms for Reconstruction of Gene Networks

A Phenomic Approach to Genetic Algorithms for Reconstruction...

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3rd International Conference on Contemporary Computing

作者： D'Souza, Rio G. L. Sekaran, K. Chandra Kandasamy, A. St Joseph Engn Coll Mangalore 575028 India Natl Inst Technol Kaarnataka Mangalore 575025 India

ISBN: (纸本)9783642148330

Genetic algorithms require a fitness function to evaluate individuals in a population. The fitness function essentially captures the dependence of the phenotype on the genotype. In the Phenomic approach we represent the phenotype of each individual in a simulated environment where phenotypic interactions are enforced. In reconstruction type of problems, the model is reconstructed from the data that maps the input to the output. In the phenomic algorithm, we use this data to replace the fitness function. Thus we achieve survival-of-the-fittest without the need for a fitness function. Though limited to reconstruction type problems where such mapping data is available, this novel approach nonetheless overcomes the daunting task of providing the elusive fitness function, which has been a stumbling block so far to the widespread use of genetic algorithms. We present an algorithm called Integrated Pheneto-Genetic Algorithm (IPGA), wherein the genetic algorithm is used to process genotypic information and the phenomic algorithm is used to process phenotypic information, thereby providing a holistic approach which completes the evolutionary cycle. We apply this novel evolutionary algorithm to the problem of elucidation of gene networks from microarray data. The algorithm performs well and provides stable and accurate results when compared to some other existing algorithms.

关键词： Genetic algorithms Phenomic algorithms Reconstruction Fitness function multiobjective evolutionary algorithms Gene networks

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Multi-objective evolutionary technique based intelligent controllers: Application to control of multivariable nonlinear systems

Multi-objective evolutionary technique based intelligent con...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Rahmati, A Rashidi, F Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)0780385667

The main objective of this paper is to present a new method based on multiobjective evolutionary algorithm for control of the multivariable and nonlinear systems. Problem design considers time domain specifications such as overshoot, rising time, settling time and stationary error as well as interaction effects. Genetic algorithms are employed to satisfy time domain design specifications that are not considered in an explicit way in the standard nonlinear control theory. Adaptation, setpoint tracking and satisfaction of temporary response specifications are the advantages of this method that be shown by some simulations.

关键词： multiobjective evolutionary algorithms multivariable nonlinear systems overshoot rise time settling time setpoint tracking

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Hybrid biobjective evolutionary algorithms for the design of a hospital waste management network

Hybrid biobjective evolutionary algorithms for the design of...

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13th Biennial Latin-Ibero-American Conference on Operations Research

作者： Medaglia, Andres L. Villegas, Juan G. Rodriguez-Coca, Diana M. Univ Los Andes Ctr Optimizac & Probabilidad Aplicada Dept Ingn Ind Bogota Colombia Univ Antioquia Dept Ingn Ind Medellin Colombia Univ Los Andes Dept Ingn Ind Los Andes Colombia Univ Technol Troyes LOSI Troyes France Escuela Colombiana Ingn Fac Ingn Bogota Colombia

Colombian environmental authorities are exploring new alternatives for improving the disposal of hospital waste generated in the Department of Boyaca (Colombia). To design this hospital waste management network we propose a biobjective obnoxious facility location problem (BOOFLP) that deals with the existing tradeoff between a low-cost operating network and the negative effect on the population living near the waste management facilities. To solve the BOOFLP we propose a hybrid approach that combines a multiobjective evolutionary algorithm (NSGA II) with a mixed-integer program. The algorithms are compared against the Noninferior Set Estimation (NISE) method and tested on data from Boyaca's hospital waste management network and publicly available instances.

关键词： Facility location Hybrid genetic algorithms multiobjective evolutionary algorithms NSGA II multiobjective optimization

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Distributed NSGA-II Sharing Extreme Non-dominated Solutions for Constrained Knapsack Problems

Distributed NSGA-II Sharing Extreme Non-dominated Solutions ...

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International Conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO)

作者： Sato, Yuji Sato, Mikiko Goto, Haruka Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Hosei Univ Japan Soc Promot Sci Tokyo Japan

ISBN: (纸本)9781538695760

A recent trend in multiobjective evolutionary algorithms is to increase the population size to approximate the Pareto front with high accuracy. On the other hand, the NSGA-II algorithm widely used in multiobjective optimization performs non-dominated sorting in solution ranking, which means an increase in computational complexity proportional to the square of the population. This execution time becomes a problem in engineering applications. It is also difficult to achieve high speeds while maintaining the accuracy of solution searching by simply applying fast, parallel processing to standard genetic operations. In this paper, we propose NSGA-II distributed processing in a many-core environment and a migration method that shares extreme Pareto solutions of the current generation among all cores after performing compensation of the non-dominated solution set obtained by distributed processing. Using typical real-valued functions and a two-objective and three-objective constrained knapsack problem for evaluation, we show that the proposed method is effective in improving diversity in solution searching while shortening execution time and increasing the accuracy of solution searching for both real-valued functions and discrete optimization problems.

关键词： multiobjective evolutionary algorithms NSGA-II distributed processing many-core environment

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Fuzzy Co-evolutionary Genetic Algorithm and its Application in clinical nutrition decision

Fuzzy Co-Evolutionary Genetic Algorithm and its Application ...

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2nd International Conference on Advanced Design and Manufacturing Engineering (ADME 2012)

作者： Wang Gaoping Zhang Meng Henan Univ Technol Sch Informat Sci & Engn Zhengzhou 450000 Peoples R China

ISBN: (纸本)9783037855034

The fuzzy co-evolution genetic algorithm is applied to clinical nutrition decision-making optimization. By cooperative co-evolutionary algorithm, the decision problem of clinical nutrition is divided into two populations,which is combined into a complete diet recipe. In this paper, different fuzzy-based definitions of optimality and dominated solution are introduced. The corresponding extension of Co-evolutionary Genetic Algorithm, so-called Fuzzy Co-Evolution Genetic Algorithm (FCEGA), will be presented as well. To verify the usefulness of such an approach, the approach is tested on analytical test cases in order to show its validity. The solutions, provided by the proposed algorithm for the clinical nutrition diet model,are promising when compared with an existing well-known algorithm.

关键词： multiobjective evolutionary algorithms clinical nutrition decision Co-evolutionary Genetic Algorithm

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