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检索条件"主题词=multi-objective evolutionary algorithm"

共 545 条记录，以下是431-440 订阅

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A Large Scale multi-objective Ontology Matching Framework 13th

A Large Scale Multi-objective Ontology Matching Framework

引用

13th International Conference on Intelligent Information Hiding and multimedia Signal Processing (IIH-MSP)

作者： Xue, Xingsi Ren, Aihong Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou Fujian Peoples R China Baoji Univ Arts & Sci Dept Math Baoji Shaanxi Peoples R China

ISBN: (纸本)9783319638560;9783319638553

multi-objective evolutionary algorithm (MOEA) is emerging as a state-of-the-art methodology to solve the ontology meta-matching problem. However, the huge search scale of large scale ontology matching problem stops MOEA based ontology matching technology from correctly and completely identifying the semantic correspondences. To this end, in this paper, a large scale multi-objective ontology matching framework is proposed, which works with three sequential steps: (1) partition the large scale ontologies into similar ontology segment pairs;(2) utilize MOEA to match the similar ontology segments in parallel;(3) select the representative ontology segment alignments, which are further aggregated to obtain the final ontology alignment. In addition, a novel multi-objective model is also constructed for ontology matching problem and the MOEA and entity similarity measure that could be used in this framework are also recommended. The experimental result shows the effectiveness of our proposal.

关键词： multi-objective evolutionary algorithm Large scale ontology matching Ontology partition

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Proposing an Efficient Combination of Interesting Measures for Mining Association Rules via NSGA-II

Proposing an Efficient Combination of Interesting Measures f...

引用

International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Rokh, Babak Mirvaziri, Hamid Eftekhari, Mahdi Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran

ISBN: (纸本)9781479980215

Selecting accurate and simple association rules that efficiently cover all data samples is very important in knowledge discovery. There are several measures to assess accuracy and relations in a rule. This poses a challenge for researchers to select effective measures. Combining different measures via multi-objective evolutionary algorithms is an effective method to select suitable association rules. Therefore in this paper NSGAII algorithm is employed for rule selection via different combination of existing measures (support, certainty factor, change of support, Yao and Liu's one way support, cosine and lift) as objectives. The contributions of the paper are twofold. Firstly, some existing measures are modified. Secondly, several experiments are done to evaluate the performance of different combinations of measures through NSGA-II. The experimental results show that the combination of certainty factor and square of cosine measures are more effective in rule selection.

关键词： Association rules interesting measures multi-objective evolutionary algorithm NSGA-II

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ONLINE DISCOVERY OF LOCOMOTION MODES FOR WHEEL-LEGGED HYBRID ROBOTS: A TRANSFERABILITY-BASED APPROACH

ONLINE DISCOVERY OF LOCOMOTION MODES FOR WHEEL-LEGGED HYBRID...

引用

14th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR 2011)

作者： Koos, S. Mouret, J. -B. Univ Pierre & Marie Curie Paris 6 ISIR CNRS UMR 7222 F-75005 Paris France

ISBN: (纸本)9789814374279

Wheel-legged hybrid robots promise to combine the efficiency of wheeled robots with the versatility of legged robots: they are able to roll on simple terrains, to dynamically adapt their posture and even to walk on uneven grounds. Although different locomotion modes of such robots have been studied, a pivotal question remains: how to automatically adapt the locomotion mode when the environment changes? We here propose that the robot autonomously discovers its locomotion mode using optimization-based learning. To that aim, we introduce a new algorithm that relies on a forward model and a stochastic multi-objective optimization. Three objectives are optimized: (1) the average displacement speed, (2) the expended energy and (3) the transferability score, which reflects how well the behavior of the robot is in agreement with the predictions of the forward model. This transferability function is approximated by conducting 20 experiments of one second on the real robot during the optimization. In the three investigated situations (flat ground, grass-like terrain, tunnel-like environment), our method found efficient controllers for forward locomotion in 1 to 2 minutes: the robot used its wheels on the flat ground, it walked on the grass-like terrain and moved with a lowered body in the tunnellike environment.

关键词： Hybrid robot Learning multi-objective evolutionary algorithm Transferability

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Gene Selection Using multi-objective Genetic algorithm Integrating Cellular Automata and Rough Set Theory

Gene Selection Using Multi-objective Genetic Algorithm Integ...

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4th International Conference on Swarm, evolutionary, and Memetic Computing (SEMCCO)

作者： Pati, Soumen Kumar Das, Asit Kumar Ghosh, Arka St Thomas Coll Engn & Technol Dept Comp Sci Informat Technol Kolkata 700023 India Bengal Engn & Sci Univ Dept Comp Sci & Technol Sibpur 711103 Howrah India Bengal Engn & Sci Univ Purabi Sch Informat Technol Sibpur 03 Howrah India

ISBN: (纸本)9783319037554;9783319037561

Feature selection is one of the most key problems in the field of machine learning and data mining. It can be done in mainly two different ways, namely, filter approach and wrapper approach. Filter approach is independent of underlying classifier logic and relatively less costly than the wrapper approach which is classifier dependent. Many researchers have applied Genetic algorithm (GA) as wrapper approach for feature selection. In the paper, a novel feature selection method is proposed based on the multi-objective genetic algorithm which is applied on population generated by non-linear uniform hybrid cellular automata. The fitness functions are defined one using set lower bound approximation of rough set theory and the other using Kullbak-Leibler divergence method. A comparative study between proposed method and some leading feature selection methods are given using some popular microarray cancer dataset to demonstrate the effectiveness of the method.

关键词： Feature selection Genetic algorithm multi-objective evolutionary algorithm Set lower bound approximation Kullback-Leibler divergence

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A Surrogate-Assisted Offspring Generation Method for Expensive multi-objective Optimization Problems

A Surrogate-Assisted Offspring Generation Method for Expensi...

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

作者： Li, Fan Gao, Liang Shen, Weiming Cai, Xiwen Huang, Shifeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Natl CNC Engn Technol Res Ctr Sch Mech Sci & Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781728169293

Surrogate-assisted multi-objective evolutionary algorithms have been commonly used to solve multi-objective expensive problems. In this paper, we investigate whether the surrogate-assisted offspring generation method can improve the optimization efficiency of multi-objective evolutionary algorithms. We first construct a surrogate model for each objective function. After that, some candidate solutions from the surrogate models are used to produce promising offspring for the multi-objective evolutionary algorithm. In addition, a pre-screening criterion based on reference vectors and the non-dominated rank is used to select the surviving offspring and exactly evaluated individuals. The pre-screening criterion can ensure the diversity and convergence of the offspring, and reduce function evaluations. Benchmark problems with their dimensions varying from 8 to 30 are used to test the effects of the surrogate-assisted offspring generation method under the framework of using the pre-screening criterion. Experimental results show that using the candidate solutions from surrogate models can enhance the performance of its basic algorithm on most of the problems.

关键词： Expensive problem multi-objective evolutionary algorithm offspring generation surrogate model

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Web Usage Mining for Improving Students Performance in Learning Management Systems

Web Usage Mining for Improving Students Performance in Learn...

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23rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Zafra, Amelia Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

ISBN: (纸本)9783642130328

An innovative technique based on multi-objective grammar guided genetic programming (MOG3P-MI) is proposed to detect the most relevant activities that a student needs to pass a course based on features extracted from logged data in an education web-based system. A more flexible representation of the available information based on multiple instance learning is used to prevent;the appearance of a great number of missing values. Experimental results with the most relevant proposals in multiple instance learning in recent years demonstrate that MOG3P-MI successfully improves accuracy by finding a balance between specificity and sensitivity values. Moreover, simple and clear classification rules winch are markedly useful to identify the number, type and time of activities that a student should do within the web system to pass a course are provided by our proposal.

关键词： Web Usage Mining Educaticutal Data Mining multiple Instance Learning multi-objective evolutionary algorithm Grammar Guided Genetic Programming

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multi-objective multiagent Credit Assignment in NSGA-II Using Difference Evaluations 14

Multi-Objective Multiagent Credit Assignment in NSGA-II Usin...

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14th International Conference on Autonomous Agents and multiagent Systems (AAMAS)

作者： Yliniemi, Logan Wilson, Drew Tumer, Kagan Oregon State Univ Corvallis OR 97331 USA Austin Peay State Univ Clarksville TN 37044 USA

ISBN: (纸本)9781450334136

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multiple exploration robots) yet there is little work on their intersection. In this work, we leverage recent advances in single-objective multiagent learning to address multi-objective domains. We focus on the impact of difference evaluation functions (which extracts an agent's contribution to the team objective) on the Non-dominated Sorting Genetic algorithm-II (NSGA-II), a state-of-the-art multi-objective evolutionary algorithm. We derive multiple methods for incorporating difference evaluations into the NSGA-II framework, and test each in a multiagent rover exploration domain, which is a good surrogate for a wide variety of distributed scheduling and resource gathering problems. We show that how and where difference evaluations are incorporated in the NSGA-II algorithm is critical, and can either provide significant benefits or destroy system performance, depending on how it is used. Median performance of the correctly used difference evaluations dominates best-case performance of NSGA-II in a multiagent multi-objective problem.

关键词： multiagent Learning multi-objective Problem multi-objective evolutionary algorithm NSGA-II

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multi-objective Genetic algorithm Based on the Correlation coefficient and Its Application

Multi-Objective Genetic Algorithm Based on the Correlation c...

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20th Chinese Control and Decision Conference

作者： Li, Junfeng Dai, Wenzhan Yang, Ye Zhejiang Sci Tech Univ Dept Automat Control Hangzhou 310018 Peoples R China

ISBN: (纸本)9781424417339

In this paper, based on the correlation coefficient, a new multi-objective evolutionary algorithm is put forward. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the correlation coefficient between every individual and the referenced vector is solved and the correlation coefficient is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

关键词： The Correlation Coefficient multi-objective evolutionary algorithm Adaptive Genetic algorithm

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Augmenting Interactive Evolution with multi-objective Optimization 14

Augmenting Interactive Evolution with Multi-Objective Optimi...

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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Christman, Joshua R. Woolley, Brian G. Air Force Inst Technol Dept Elect & Comp Engn Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781509002870

Deceptive fitness landscapes are a growing concern for the field of evolutionary computation. Recent work has demonstrated that incorporating human insights with short-term automated evolution has a synergistic effect that eases deception and accelerates the discovery of solutions. While human evaluators provide rich insight into a domain, they fatigue easily. Previous work reduced the number of human evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, it lacked the ability to measure solution qualities that the human evaluator implicitly identified as important. The key insights of this paper are that multi-objective evolutionary algorithms (MOEAs) foster diversity and that the non-dominated set can serve as a surrogate for novelty while measuring user preferences data. This new approach, called Pareto Optimality-Assisted Interactive evolutionary Computation (POA-IEC), leverages human intuitions by allowing users to identify candidates in the non-dominated set that they feel are promising. Interestingly, the experimental results demonstrate that POA-IEC finds solutions in significantly fewer evaluations than previous approaches, and that the non-dominated set contains significantly more novel behaviors than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences.

关键词： Interactive evolutionary computation multi-objective evolutionary algorithm Novelty search Serendipitous discovery

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Simulation-Driven multi-objective Evolution for Traffic Light Optimization 1

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23rd European International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Cacco, Alessandro Iacca, Giovanni Univ Trento Dept Informat Engn & Comp Sci I-38123 Povo Italy

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

The constant growth of vehicles circulating in urban environments poses a number of challenges in terms of city planning and traffic regulation. A key aspect that affects the safety and efficiency of urban traffic is the configuration of traffic lights and junctions. Here, we propose a general framework, based on a realistic urban traffic simulator, SUMO, to aid city planners to optimize traffic lights, based on a customized version of NSGA-II. We show how different metrics -such as number of accidents, average speed of vehicles, and number of traffic jams- can be taken into account in a multi-objective fashion to obtain a number of Pareto-optimal light configurations. Our experiments, conducted on two city scenarios in Italy and different combinations of fitness functions, demonstrate the validity of this approach and show how evolutionary optimization is an effective tool for traffic light optimization.

关键词： Traffic light optimization Simulation of urban mobility multi-objective evolutionary algorithm

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