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On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems

BIOSYSTEMS 2005年第2期81卷 101-112页

作者： Setzkorn, C Paton, RC Univ Liverpool Dept Comp Sci Liverpool L7 8XP Merseyside England

Extracting comprehensible and general classifiers from data in the form of rule systems is an important task in many problem domains. This study investigates the utility of a multi-objective evolutionary algorithm (MOEA) for this task. multi-objective evolutionary algorithms are capable of finding several trade-off solutions between different objectives in a single run. In the context of the present study, the objectives to be optimised are the complexity of the rule systems, and their fit to the data. Complex rule systems are required to fit the data well. However, overly complex rule systems often generalise poorly on new data. In addition they tend to be incomprehensible. It is, therefore, important to obtain trade-off solutions that achieve the best possible fit to the data with the lowest possible complexity. The rule systems produced by the proposed multi-objective evolutionary algorithm are compared with those produced by several other existing approaches for a number of benchmark datasets. It is shown that the algorithm produces less complex classifiers that perform well on unseen data. (c) 2005 Elsevier Ireland Ltd. All rights reserved.

关键词： supervised classification multi-objective evolutionary algorithms fuzzy classification rule systems

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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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Optimization of ballast plan in launch jacket load-out

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2009年第3期38卷 267-288页

作者： Kurniawan, Adi Ma, Guowei Nanyang Technol Univ Sch Civil & Environm Engn Maritime Res Ctr Singapore 639798 Singapore

The calculation of ballast plan in load-out operations is generally performed assuming a rigid barge. This assumption may not be reliable since in reality the barge is flexible. Having incorrect ballast plan may lead to overstressing of the loaded-out structure. We present a method of finding a more accurate ballast plan, taking into account the flexibility of the barge. This method makes use of a multi-objective evolutionary algorithm to find the optimum ballast arrangement at every load-out stage. We model the load-out configuration as a beam on elastic foundation, loaded with distributed trapezoidal loading representing the load from the structure. Minimizing deflection and curvature of the beam, as well as maximizing the ballast transfer efficiency between the load-out stages are chosen as the objectives of the algorithm. It is shown that the proposed method is better than the conventional rigid barge method in terms of minimizing the deflection and curvature as well as maximizing the ballast transfer efficiency.

关键词： Load-out Ballast Barge Beam on elastic foundations multi-objective evolutionary algorithms

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A Three-objective evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems

A Three-Objective Evolutionary Approach to Generate Mamdani ...

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4th International Workshop on Hybrid Artificial Intelligence Systems

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

ISBN: (纸本)9783642023187

In the last years, several papers have proposed to adopt multi-objective evolutionary algorithms (MOEAs) to generate Mamdani fuzzy rule-based systems with different trade-offs between interpretability and accuracy. Since interpretability is difficult to quantify because of its qualitative nature, several measures have been introduced, but there is no general agreement on any of them. In this paper, we propose all MOEA to learn concurrently rule base and membership function parameters by optimizing accuracy and interpretability, which is measured in terms of number of conditions in the antecedents of rules and partition integrity. Partition integrity is evaluated by using a purposely-defined index based on the piecewise linear transformation exploited to learn membership function parameters. Results on a real-world regression problem are shown and discussed.

关键词： Accuracy-Interpretability Trade-off Interpretability Index multi-objective evolutionary algorithms Piecewise Linear Transformation

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multi-objective design and control of hybrid systems minimizing costs and unmet load

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第1期79卷 170-180页

作者： Bernal-Agustin, Jose L. Dufo-Lopez, Rodolfo Univ Zaragoza Dept Elect Engn Zaragoza 50018 Spain

This paper presents, for the first time, the application of the strength Pareto evolutionary algorithm to the multi-objective design of isolated hybrid systems, minimising both the total cost throughout the useful life of the installation and the unmet load. For this task. a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combinations of components for the hybrid system and control strategy. Also, a novel control strategy has been developed and it will be expounded in this article. As an example of application, a PV-wind-diesel system has been designed, obtaining a Set of possible Solutions (Pareto set) from which the designer can choose those which he/she prefers considering the costs and unmet load of each. The results obtained demonstrate the practical utility of the design method used. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Hybrid systems multi-objective design multi-objective evolutionary algorithms Genetic algorithms

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A comparison between dynamic weighted aggregation and NSGA-II for multi-objective evolutionary algorithms

A comparison between dynamic weighted aggregation and NSGA-I...

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IASTED International Conference on Computational Intelligence

作者： Drzadzewski, G Wineberg, M Univ Guelph Guelph ON N1G 2W1 Canada

ISBN: (纸本)0889864799

The performance of the Dynamic Weight Aggregation system as applied to a Genetic Algorithm (DWAGA) and NSGA-II are evaluated and compared against each other. The algorithms are run on 11 test functions. The performance of the algorithms is evaluated by examining the spacing, diversity and coverage of the Pareto front, as well as each algorithm's execution time. It is discovered that, while the NSGA-II performs better on most of the test functions, the DWAGA can outperform the NSGA-II on some of the functions, including a concave one.

关键词： multi-objective evolutionary algorithms genetic algorithms

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Quality-time analysis of multi-objective evolutionary algorithms 05

Quality-time analysis of multi-objective evolutionary algori...

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Genetic and evolutionary Computation Conference

作者： Chen, Jian-Hung Ho, Shinn-Ying Goldberg, David E. Acad Sinica Inst Informat Sci Taipei 115 Taiwan

ISBN: (纸本)1595930108

A quality-time analysis of multi-objective evolutionary algorithms (MOEAs) based on schema theorem and building blocks hypothesis is developed. A bicriteria OneMax problem, a hypothesis of niche and species, and a definition of dissimilar schemata are introduced for the analysis. In this paper, the convergence time, the first and last hitting time models are constructed for analyzing the performance of MOEAs. Population sizing model is constructed for determining appropriate population sizes. The models are verified using the bicriteria OneMax problem. The theoretical results indicate how the convergence time and population size of a MOEA scale up with the problem size, the dissimilarity of Pareto-optimal solutions, and the number of Pareto-optimal solutions of a multi-objective optimization problem.

关键词： convergence dissimilar schemata multi-objective evolutionary algorithms population sizing

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Set-based concept selection in multi-objective problems: optimality versus variability approach

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JOURNAL OF ENGINEERING DESIGN 2009年第3期20卷 217-242页

作者： Avigad, Gideon Moshaiov, Amiram ORT Braude Coll Engn Dept Mech Engn Karmiel Israel Tel Aviv Univ Dept Mech Engn IL-69978 Tel Aviv Israel

This paper presents a novel approach to support the selection of conceptual solutions to multi-objective problems. The proposed method involves a comparison between concepts, based on the performances of sets of solutions that represent them. The set-based comparison of concepts is consistent with the so-called Toyota set-based concurrent engineering process. Such an approach discourages early exploitation of solutions and promotes extended exploration of the design space by means of sets of solutions. Both optimality and variability of concepts are considered, and their measures are devised to pose the selection problem as an auxiliary multi-objective problem. The auxiliary objectives are to maximise optimality and to maximise the variability. This highlights the inherent multi-objectivity of concept selection and supports decision-making under the possible contradictory nature of optimality and variability of concepts. Both academic and engineering problems are used to demonstrate the approach and to expose the inherent subjectivity of the measures, which are dependent on the selection of a window of interest by the decision-makers.

关键词： multi-objective optimisation multi-criteria decision-making set-based measures multi-objective evolutionary algorithms conceptual design

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multi-objective Feature Selection in QSAR Using a Machine Learning Approach

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QSAR & COMBINATORIAL SCIENCE 2009年第11-12期28卷 1509-1523页

作者： Soto, Axel J. Cecchini, Rocio L. Vazquez, Gustavo E. Ponzoni, Ignacio Univ Nacl Sur Dept Ciencias & Ingn Computac LIDeCC RA-8000 Bahia Blanca Buenos Aires Argentina Univ Nacl Sur CONICET RA-8000 Bahia Blanca Buenos Aires Argentina

The selection of descriptor subsets for QSAR/QSPR is a hard combinatorial problem that requires the evaluation of complex relationships in order to assess the relevance of the selected subsets. In this paper, we describe the main issues in applying descriptor selection for QSAR methods and propose a novel two-phase methodology for this task. The first phase makes use of a multi-objective evolutionary technique which yields interesting advantages compared to mono-objective methods. The second phase complements the first one and it enables to refine and improve the confidence in the chosen subsets of descriptors. This methodology allows the selection of subsets when a large number of descriptors are involved and it is also Suitable for linear and nonlinear QSAR/QSPR models. The proposed method was tested using three data sets with experimental values for blood-brain barrier penetration, human intestinal absorption and hydrophobicity. Results reveal the capability of the method for achieving subsets of descriptors with a high predictive capacity and a low cardinality. Therefore, our proposal constitutes a new promising technique helpful for the development of QSAR/QSPR models.

关键词： Descriptor selection multi-objective evolutionary algorithms Bayesian regularized neural networks Computational chemistry Medicinal chemistry

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multi-objective Query Optimization Using Topic Ontologies

Multi-objective Query Optimization Using Topic Ontologies

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8th International Conference on Flexible Query Answering Systems

作者： Cecchini, Rocio L. Lorenzetti, Carlos M. Maguitman, Ana G. Univ Nacl Sur Dept Ciencias & Ingn Computac RA-8000 Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783642049569

Formulating search queries based on a thematic context is a challenging problem due to the large number of combinations in which terms can be used to reflect the topic of interest. This paper presents a novel approach to learn topical queries that simultaneously satisfy multiple retrieval objectives. The proposed method consists in using a topic ontology to train an evolutionary Algorithm that incrementally moves a population of queries towards the proposed objectives. We present an analysis of different single- and multi-objective strategies, discuss their strengths and limitations and test the most promising strategies on a large set of labeled Web pages. Our evaluations indicate that the tested strategies that apply multi-objective evolutionary algorithms are significantly superior to a baseline approach that attempts to generate queries directly from a topic description.

关键词： topic ontologies topical queries semantic similarity multi-objective evolutionary algorithms query effectiveness

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