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

共 321 条记录，以下是141-150 订阅

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A multi-objective Neuro-evolutionary Algorithm to Obtain Interpretable Fuzzy Models

A Multi-objective Neuro-evolutionary Algorithm to Obtain Int...

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13th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2009)

作者： Sanchez, Gracia Jimenez, Fernando Sanchez, Jose F. Alcaraz, Jose M. Univ Murcia Dept Ingn Informac & Comunicac E-30001 Murcia Spain

ISBN: (纸本)9783642142635

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. A multi-objective evolutionary algorithm is implemented with three different selection and generational replacements schemata (Niched Preselection, NSGA-II and ENORA) to generate fuzzy models in the proposed optimization context. The results clearly show a real ability and effectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models. These schemata have been compared in terms of accuracy, interpretability and compactness by using three test problems studied in literature. Statistical tests have also been used with optimality and diversity multi-objective metrics to compare the schemata.

关键词： TSK Fuzzy Models multi-objective evolutionary algorithms RBF Neural Networks

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Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via evolutionary multi-objective Model Compression

Balancing Performance, Efficiency and Robustness in Open-Wor...

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International Joint Conference on Neural Networks (IJCNN)

作者： Del Ser, Javier Martinez-Seras, Aitor Bilbao, Miren Nekane Lobo, Jesus L. Lana, Ibai Herrera, Francisco Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

ISBN: (纸本)9798350359329;9798350359312

When deploying machine learning models on resource-constrained hardware, reducing the memory footprint required by the model without compromising its performance is critical. Moreover, in open-world scenarios models often operate in dynamic and unpredictable environments where the data distribution evolves over time. Robust models can generalize well to unforeseen circumstances, including out-of-distribution inputs that may not have been encountered during the training phase. This adaptability is essential to handle the inherent variability of real-world data. This work formulates a multi-objective optimization problem that aims at optimizing the quantization resolution of the parameters of an already trained machine learning model based on three conflicting goals: maximizing the performance of the model on its designated learning task, minimizing the memory footprint of the compressed model, and enhancing its robustness against out-of-distribution data. Given the complexity of the resulting combinatorial optimization problem, we employ multi-objective evolutionary algorithms to efficiently obtain an approximation of the Pareto front balancing among the aforementioned objectives. Experiments with a randomized neural network compressed under the proposed formulation are run over several benchmark classification datasets. Different multi-objective solvers are employed to compare their effectiveness in terms of the convergence and diversity of their produced Pareto estimations. Additionally, we assess the achieved equilibrium between the three objectives against a floating-point implementation of the same model. Our experiments reveal that both the computational resources and the robustness of the model can be optimized via evolutionary quantization without significantly sacrificing its performance for the task at hand.

关键词： Model Compression AI Safety Green AI Out-ofDistribution Detection multi-objective evolutionary algorithms Randomization-based Neural Networks.

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Network power saving based on Pareto optimal control with evolutionary approach

Network power saving based on Pareto optimal control with ev...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Akishita, Yosuke Ohsita, Yuichi Murata, Masayuki Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781509045884

The power consumption of networks has been increasing as the service over the Internet becomes popular, and has become a serious problem. Many methods to reduce the power consumption by shutting down unnecessary network devices following the environmental changes have been proposed. These methods consider only simple objectives such as the number of powered-on nodes and the maximum link utilization. However, multiple complex objectives such as delay and reliability should be also considered in the actual network. In this paper, we propose a network power saving method that handles multiple complex objectives, following the environmental changes. In this method, we store the candidate network configurations, and evolve them, following the environmental changes. Then, we select the network configuration from the candidate network configurations. We combine two approaches to evolve the network configurations. The first approach is based on Pareto optimal, and evolves the network configurations so as to be close to the Pareto optimal solutions, considering multiple objectives. Another approach is based on the diversity of the network configurations. By storing the diverse network configurations, we can handle the significant environmental changes. We evaluate our method by simulation, and demonstrate that our method reduces the power consumption without violating the constraints, following the traffic changes. In addition, we also demonstrate that our method can keep the connectivity in case of failures, and recover the performance and the small power consumption soon after the failure occurs.

关键词： Network power saving evolutionary approach Pareto optimal solutions Pareto front multi-objective evolutionary algorithms

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A Parallel Implementation of a multi-objective evolutionary Algorithm

A Parallel Implementation of a Multi-objective Evolutionary ...

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9th International Conference on Information Technology and Applications in Biomedicine

作者： Kannas, Christos C. Nicolaou, Christos A. Pattichis, Constantinos S. Univ Cyprus Noesis Chemoinformat CY-1678 Nicosia Cyprus Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Cyprus Cyprus Inst Noesis Chemoinformat CY-1678 Nicosia Cyprus

ISBN: (纸本)9781424453788

multi-objective evolutionary algorithms (MOEAs) have features that can be exploited to harness the processing power offered by modern multi-core CPUs. Modern programming languages offer the ability to use threads and processes in order to achieve parallelism that is inherent in multi-core CPUs. In this paper we present our Parallel implementation of a MOEA algorithm and its application to the de novo drug design problem. The results indicate that using multiple processes that execute independent tasks of a MOEA, can reduce significantly the execution time required and maintain comparable solution quality thereby achieving improved performance.

关键词： evolutionary algorithms multi-objective evolutionary algorithms Parallel evolutionary algorithms Parallel multi-objective evolutionary algorithms Parallel Processing

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multi-objective evolutionary Optimization for Generating Ensembles of Classifiers in the ROC Space 12

Multi-Objective Evolutionary Optimization for Generating Ens...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Levesque, Julien-Charles Durand, Audrey Gagne, Christian Sabourin, Robert Univ Laval Dep Genie Elect & Genie Informat Lab Vis & Syst Numer Quebec City PQ Canada

ISBN: (纸本)9781450311779

In this paper, we propose a novel approach for the multi-objective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.

关键词： Machine learning ROC curve multi-objective evolutionary algorithms Ensembles of classifiers Genetic programming

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A multi-objective evolutionary Algorithm for Tuning Fuzzy Rule-Based Systems with Measures for Preserving Interpretability

A Multi-objective Evolutionary Algorithm for Tuning Fuzzy Ru...

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Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT)

作者： Gacto, M. J. Alcala, R. Herrera, F. Univ Jaen Dept Comp Sci Jaen Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

ISBN: (纸本)9789899507968

In this contribution we propose a multi-objective evolutionary algorithm for Tuning Fuzzy Rule-Based Systems by considering two objectives, accuracy and interpretability. To this aim we define a new objective that allows preserving the interpretability of the system. This new objective is an interpretability index which is the union of three metrics to preserve the original shapes of the membership functions as much as possible while a tuning of the membership function parameters is performed. The proposed method has been compared to a single objective accuracy-guided algorithm in two real problems showing that many solutions in the Pareto front dominate to those obtained by the single objective-based one.

关键词： Fuzzy Rule-Based Systems Tuning Interpretability multi-objective evolutionary algorithms

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Using multiobjective evolutionary algorithms to Understand Parkinson's Disease

Using Multiobjective Evolutionary Algorithms to Understand P...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Vallejo, Marta Cosgrove, Jeremy Alty, Jane E. Smith, Stephen L. Corne, David W. Lones, Michael A. Heriot Watt Univ Dept Comp Sci Edinburgh EH14 4AS Midlothian Scotland Leeds Gen Infirm Dept Neurol Leeds W Yorkshire England Univ York Dept Elect York YO10 5DD N Yorkshire England

ISBN: (纸本)9781450343237

The incidence of neurodegenerative diseases such as Parkinson's is increasing rapidly around the world, yet the symptoms and pathology of these diseases remain incompletely understood. As a consequence, it is challenging for clinicians to provide patients with accurate diagnoses or prognoses. In this work, we use multi-objective evolutionary algorithms to explore recordings of patients drawing neurological assessment figures, with the aim of identifying patterns of cognitive and motor signals that discriminate different disease states. As a proof of principle, we demonstrate how this approach can be used to explore the trade-off between predicting clinical measures of motor and cognitive deficit.

关键词： multi-objective evolutionary algorithms Predictive modelling Parkinson's disease Polynomial regression

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A multi-objective evolutionary Action Rule Mining Method

A Multi-Objective Evolutionary Action Rule Mining Method

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IEEE Congress on evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Daly, Grant Benton, Ryan Johnsten, Tom Univ S Alabama Dept Comp Sci Sch Comp Mobile AL 36688 USA

ISBN: (纸本)9781509060177

Action rules are rules that describe how to transition a decision attribute from an undesired state to a desired state, with the understanding that some attributes are stable and others are flexible. Stable attributes, such as "age", may not be changed, whereas flexible attributes, such as "interest rate", may be changed. Action rules have great potential in data mining, as they output easily interpretable rules which can immediately be useful to a decision maker. However, at present, the methods to generate all valid action rules are computationally expensive. To address this, methods have been proposed that prune swaths of the search space as rules are generated;this results in computational efficiency, at the expense of potentially not discovering many useful rules. In this work, a method, called multi-objective evolutionary Action Rule (MOEAR) mining, is introduced. MOEAR optimizes the discovery of action rules using standard evolutionary algorithm principles. Experimental results show that MOEAR is able to generate a large number of potentially interesting action rules, including those rules that could be categorized as "rare", while achieving good computational performance.

关键词： Action Rules multi-objective evolutionary algorithms

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A New Many-objective evolutionary Algorithm Based on Self-Adaptive Differential Evolution

A New Many-Objective Evolutionary Algorithm Based on Self-Ad...

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9th International Conference on Natural Computation (ICNC)

作者： Zhao, Hongyan Xiao, Jing Liaoning Prov Coll Commun Coll Informat Engn Shenyang Peoples R China

ISBN: (纸本)9781467347143

To improve the performance of the existing multi-objective evolutionary algorithms (MOEAs), we propose a new self-adaptive differential evolution algorithm for solving many-objective optimization problems (MOPs). To address the challenges in many-objective optimization, new selection strategy and density estimation method are designed to improve the performance of the elite MOEA model used by several exiting MOEAs. In addition, new mutation strategy and parameter adaptive method of DE are proposed to enhance the convergence ability of the evolution strategy utilized in MOEAs. Experimental results on ZDT and DTLZ test problems show that, the proposed algorithm, named SDEMO, is able to find much better spread of solutions with better approximating the true Pareto-optimal front compared to six state-of-the-art MOEAs.

关键词： multi-objective evolutionary algorithms many-objective optimization differential evolution elite selection strategy crowding density estimation

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On the Many-objective Pickup and Delivery Problem: Analysis of the Performance of Three evolutionary algorithms 16th

On the Many-Objective Pickup and Delivery Problem: Analysis ...

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16th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Garcia-Najera, Abel Lopez-Jaimes, Antonio Zapotecas-Martinez, Saul Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Av Vasco de Quiroga 4871 Ciudad De Mexico 05300 Mexico

ISBN: (纸本)9783030028374;9783030028367

Many-objective optimization focuses on solving optimization problems with four or more objectives. Effort has been made mainly on studying continuous problems, with interesting results and for which several optimizers have been proposed. Nevertheless, combinatorial problems have not received as much attention, making this an open research area. An important result on continuous problems states that the problem does not necessarily becomes more difficult while more objectives are considered, but, does this result hold for combinatorial problems? This investigation takes this subject on by studying a many-objective combinatorial problem, particularly, the pickup and delivery problem (PDP), which is an important combinatorial optimization problem in the transportation industry and consists in finding a collection of routes with minimum cost. Traditionally, cost has been associated with the number of routes and the total travel distance, however, some other objectives emerge in many applications, for example, travel time, workload imbalance, and uncollected profit. If we consider all these objectives equally important, PDP can be tackled as a many-objective problem. This study is concerned with the study of the performance of three multi-objective evolutionary algorithms on the PDP varying the number of objectives, in order to analyze the change of PDP's difficulty when the number of objectives is increased. Results show that the problem becomes more difficult to solve while more objectives are considered.

关键词： Many-objective optimization Pickup and delivery problem multi-objective evolutionary algorithms Combinatorial optimization

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