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

共 321 条记录，以下是261-270 订阅

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Revisiting the NSGA-II Crowding-Distance Computation 13

Revisiting the NSGA-II Crowding-Distance Computation

引用

15th Genetic and evolutionary Computation Conference (GECCO)

作者： Fortin, Felix-Antoine Parizeau, Marc Univ Laval Dept Genie Elect & Genie Informat Lab Vis & Syst Numer Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781450319638

This paper improves upon the reference NSGA-II procedure by removing an instability in its crowding distance operator. This instability stems from the cases where two or more individuals on a Pareto front share identical fitnesses. In those cases, the instability causes their crowding distance to either become null, or to depend on the individual's position within the Pareto front sequence. Experiments conducted on nine different benchmark problems show that, by computing the crowding distance on unique fitnesses instead of individuals, both the convergence and diversity of NSGA-II can be significantly improved.

关键词： multi-objective evolutionary algorithms NSGA-II crowding distance

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Learning Concurrently Granularity, Membership Function Parameters and Rules of Mamdani Fuzzy Rule-based Systems

Learning Concurrently Granularity, Membership Function Param...

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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： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elettron I-56122 Pisa Italy

ISBN: (纸本)9789899507968

In this paper we tackle the issue of generating Mamdani fuzzy rule-based systems with optimal trade-offs between complexity and accuracy by using a multi-objective genetic algorithm, which concurrently learns rule base, granularity of the input and output partitions and membership function parameters. To this aim, we exploit a chromosome composed of three parts, which codify, respectively, the rule base, and, for each variable, the number of fuzzy sets and the parameters of a piecewise linear transformation of the membership functions. We show the encouraging results obtained on a real world regression problem.

关键词： Accuracy-Interpretability Trade-off Granularity Learning Mamdani Fuzzy-Rule-Based Systems multi-objective evolutionary algorithms Piecewise Linear Transformation

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A multi-objective Approach to Predicting Motor and Cognitive Deficit in Parkinson's Disease Patients

A Multi-Objective Approach to Predicting Motor and Cognitive...

引用

Genetic and evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9781450343237

Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due to the wide range of symptoms, including significant overlap with other neurodegenerative conditions, both diagnosis and prognosis remain challenging. In this paper, we describe our use of a multi-objective evolutionary algorithm to explore trade-offs between polynomial regression models that predict different clinical measures, with the aim of identifying features that are most indicative of motor and cognitive PD variants. Our initial results are promising, showing that polynomial regression models are able to predict clinical measures with good accuracy, and that suitable predictive features can be identified.

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

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Adaptive Bi-objective Genetic Programming for Data-Driven System Modeling

Adaptive Bi-objective Genetic Programming for Data-Driven Sy...

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12th International Conference on Intelligent Computing (ICIC)

作者： Bevilacqua, Vitoantonio Nuzzolese, Nicola Mininno, Ernesto Iacca, Giovanni Politecn Bari Dipartimento Ingn Elettr & Informaz Via Orabona 4 I-70125 Bari Italy Cyber Dyne Srl Via Scipione Crisanzio 119 I-70123 Bari Italy

ISBN: (纸本)9783319422978;9783319422961

We propose in this paper a modification of one of the modern state-of-the-art genetic programming algorithms used for data-driven modeling, namely the Bi-objective Genetic Programming (BioGP). The original method is based on a concurrent minimization of both the training error and complexity of multiple candidate models encoded as Genetic Programming trees. Also, BioGP is empowered by a predator-prey co-evolutionary model where virtual predators are used to suppress solutions (preys) characterized by a poor trade-off error vs complexity. In this work, we incorporate in the original BioGP an adaptive mechanism that automatically tunes the mutation rate, based on a characterization of the current population (in terms of entropy) and on the information that can be extracted from it. We show through numerical experiments on two different datasets from the energy domain that the proposed method, named BioAGP (where "A" stands for "Adaptive"), performs better than the original BioGP, allowing the search to maintain a good diversity level in the population, without affecting the convergence rate.

关键词： multi-objective evolutionary algorithms Adaptive genetic programming Machine learning Home automation Energy efficiency

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Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper objectives 35

Good Things Come In Threes: Improving Search-based Crash Rep...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Derakhshanfar, Pouria Devroey, Xavier Zaidman, Andy van Deursen, Arie Panichella, Annibale Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781450367684

Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.

关键词： crash reproduction search-based software testing multi-objective evolutionary algorithms

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To Preserve or Not to Preserve Invalid Solutions in Search-Based Software Engineering: A Case Study in Software Product Lines 18

To Preserve or Not to Preserve Invalid Solutions in Search-B...

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40th ACM/IEEE International Conference on Software Engineering (ICSE)

作者： Guo, Jianmei Shi, Kai Alibaba Grp Hangzhou Zhejiang Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781450356381

multi-objective evolutionary algorithms (MOEAs) have been successfully applied for software product lines (SPLs) to search for optimal or near-optimal solutions that balance multiple objectives. However, MOEAs usually produce invalid solutions that violate the constraints predefined. As invalid solutions are unbuildable in practice, we debate the preservation of invalid solutions during the search. We conduct experiments on seven real-world SPLs, including five largest SPLs hitherto reported and two SPLs with realistic values and constraints of quality attributes. We identify three potential limitations of preserving invalid solutions. Furthermore, based on the state-of-the-art, we design five algorithm variants that adopt different evolutionary operators. By performance evaluation, we provide empirical guidance on how to preserve valid solutions. Our empirical study demonstrates that whether or not to preserve invalid solutions deserves more attention in the community, and in some cases, we have to preserve valid solutions all along the way.

关键词： Search-based software engineering software product lines multi-objective evolutionary algorithms constraint solving validity

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Visualization of multi-objective Design Space Exploration for Embedded Systems

Visualization of Multi-Objective Design Space Exploration fo...

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13th Euromicro Conference on Digital System Design on Architectures, Methods and Tools

作者： Taghavi, Toktam Pimentel, Andy D. Univ Amsterdam Inst Informat Comp Syst Architecture Grp Amsterdam Netherlands

ISBN: (纸本)9780769541716

Modern embedded systems come with contradictory design constraints. On one hand, these systems often target mass production and battery-based devices, and therefore should be cheap and power efficient. On the other hand, they need to achieve high (real-time) performance. This wide spectrum of design requirements leads to complex heterogeneous system-on chip (SoC) architectures. The complexity of embedded systems forces designers to model and simulate systems and their components to explore the wide range of design choices. Such design space exploration is especially needed during the early design stages, where the design space is at its largest. Due to the exponential design space in real problems and multiple criteria to be considered, multi-objective evolutionary algorithms (MOEAs) are often used to trim down a large design space into a finite set of points and provide the designer a set of tradable solutions with respect to the design criteria. Interpreting the search results (e.g., where are the Pareto points located), understanding their relations and analyzing how the design space was searched by such searching algorithms is of invaluable importance to the designer. To this end, this paper presents a novel interactive visualization tool, based on tree visualization, to understand the search dynamics of a MOEA and to visualize where the optimum design points are located in the design space and what objective values they have.

关键词： component design space exploration visualization multi-objective evolutionary algorithms embedded systems

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Stochastic optimization of a neural network-based controller for aggressive maneuvers on loose surfaces

Stochastic optimization of a neural network-based controller...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Terekhov, Alexander V. Mouret, Jean-Baptiste Grand, Christophe UPMC CNRS Inst Syst Intelligents & Robot F-75005 Paris France

ISBN: (纸本)9781424466757

In this study we develop a feedback controller for a four wheeled autonomous mobile robot. The purpose of the controller is to guarantee robust performance of an aggressive maneuver (90 degrees turn) at high velocity (about 10 m/s) on a loose surface (dirty road). To tackle this highly non-linear control problem, we employ multi-objective evolutionary algorithms to explore and optimize the parameters of a neural network-based controller. The obtained controller is shown to be robust with respect to uncertainties of the robot parameters, speed of the maneuver and properties of the ground. The controller is tested using two mathematical models of significantly different complexity and accuracy.

关键词： aggressive maneuvers closed loop systems feedback feedback controller mobile robots multi-objective evolutionary algorithms neural network based controller neurocontrollers nonlinear control nonlinear control systems optimisation stochastic optimization stochastic processes wheeled autonomous mobile robot

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Adaptive PBI for Massively Parallel MOEA/D in a Distributed Memory Environment

Adaptive PBI for Massively Parallel MOEA/D in a Distributed ...

引用

IEEE Congress on evolutionary Computation (CEC)

作者： Sato, Yuji Hirayama, Tomoya Ikami, Ryo Hosei Univ Fac Comp & Informat Sci Tokyo Japan

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

This paper proposes an adaptive PBI for massively parallel MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the reference vector in the objective function space, the T-neighbor is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a method to improve the problem that the T-neighbor is divided and the solution distribution becomes sparse by adaptively controlling the penalty value in the PBI function according to the distance from the reference vector using a distribution function such as Laplace distribution. The effectiveness of the proposed method is shown by comparison with execution using a single CPU.

关键词： MOEA/D parallel and distributed processing distributed memory environment multi-objective evolutionary algorithms

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Diffusely Distributed Parallelization of MOEA/D with Edge Weight Vectors Sharing

Diffusely Distributed Parallelization of MOEA/D with Edge We...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Midtlyng, Mads Sato, Mikiko Hosei Univ Fac Comp & Informat Sci Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan

ISBN: (纸本)9798400701207

This paper proposes the partitioning method with edge weight vectors sharing for parallel distributed MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the weight vector in the objective function space, the T-neighborhood is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a partitioning method to share edge weight vectors among all partitions, and then assign other weight vectors uniformly to each partition. Using the constrained knapsack problem as a benchmark problem, we show that it is possible to eliminate the need for migration processing to correct the value of the ideal point and improve the problem that the T-neighborhood is divided, and the distribution of the solution becomes sparse.

关键词： MOEA/D parallel and distributed processing distributed memory environment multi-objective evolutionary algorithms

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