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Exploitation of a Medium-Sized Fuzzy Outranking Relation Based on multi-objective evolutionary algorithms to Derive a Ranking

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第4期9卷 745-764页

作者： Leyva Lopez, Juan Carlos Solano Noriega, Jesus Jaime Garcia Alcaraz, Jorge Luis Gastelum Chavira, Diego Alonso Univ Occidente Blvd Lola Beltran & Blvd Rolando Arjona Culiacan Sinaloa Mexico Univ Autonoma Ciudad Juarez Ave Del Charro 450 Norte Ciudad Juarez Chihuahua Mexico Univ Autonoma Sinaloa Ciudad Univ Culiacan Sinaloa Mexico

We present a multi-objective evolutionary algorithm to exploit a medium-sized fuzzy outranking relation to derive a partial order of classes of alternatives (we call it RP2-NSGA-II). To measure the performance of RP2-NSGA-II, we present an empirical study over a set of simulated multi-criteria ranking problems. The result of this study shows that RP2-NSGA-II can effectively exploit a medium-sized fuzzy outranking relation. Finally, we present a real-case study for ranking the municipalities of the state of Guanajuato, Mexico by their levels of marginalization.

关键词： multi-criteria Decision Analysis Outranking Relations multi-objective evolutionary algorithms Ranking Procedures

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

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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 Generic Framework for Incorporating Constraint Handling Techniques into multi-objective evolutionary algorithms 21st

A Generic Framework for Incorporating Constraint Handling Te...

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21st International Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Fukumoto, Hiroaki Oyama, Akira Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa 2525210 Japan

ISBN: (纸本)9783319775388

A generic framework for incorporating constraint handling techniques (CHTs) into multi-objective evolutionary algorithms (MOEAs) is proposed to resolve the differences between MOEAs from algorithmic and implementation perspective with respect to the incorporation of CHTs. To verify the effectiveness of the proposed framework, the performances of the combined algorithms of five CHTs and four MOEAs on eight constrained multi-objective optimization problems are investigated with the proposed framework. The experimental results show that the outperforming CHT can vary by constrained multi-objective optimization problems, as far as examined in this study.

关键词： Constraint handling techniques Constrained multi-objective optimization problems multi-objective evolutionary algorithms

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Investigation and comparison of the performance of multi-objective evolutionary algorithms based on decomposition and dominance in portfolio optimization 26

Investigation and comparison of the performance of multi-obj...

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Rajabi, Mahsa Khaloozadeh, Hamid KN Toosi Univ Technol Elect & Control Dept Tehran Iran

ISBN: (纸本)9781538649169

A new approach in multi-objective evolutionary optimization is decomposition. Decomposition is a basic method in old multi-objective optimization which in evolutionary multi objective optimization, has not been widely used. Using this method, a multi-objective optimization problem is converted into a number of scalar subproblems, and all the subproblems are simultaneously optimized. In this paper, the performance and efficiency of the algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) with the performance of two algorithms NSCA-II and MOPSO (evolution optimization methods based on dominance) for solving constrained portfolio optimization in Tehran Stock Exchange, has been compared. Portfolio return and its risk, has been considered as the optimization objectives and CvaR has been considered as a risk measure. The results indicate the high potential of these algorithms for constrained portfolio optimization. Also, the results indicate that the optimization algorithm based on decomposition has lower computational complexity, and Pareto front is more extensive than the other two methods.

关键词： multi-objective evolutionary algorithms portfolio optimization value at conditional risk Decomposition Dominance

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Optimizing Feed-Forward Neural Network Topology by multi-objective evolutionary algorithms: A Comparative Study on Biomedical Datasets 10th

Optimizing Feed-Forward Neural Network Topology by Multi-obj...

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10th Italian Workshop on Artificial Life and evolutionary Computation (WIVACE)

作者： Bevilacqua, Vitoantonio Cassano, Fabio 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)9783319326955;9783319326948

The design of robust classifiers, for instance Artificial Neural Networks (ANNs), is a critical aspect in all complex pattern recognition or classification tasks. Poor design choices may undermine the ability of the system to correctly classify the data samples. In this context, evolutionary techniques have proven particularly successful in exploring the complex state-space underlying the design of ANNs. Here, we report an extensive comparative study on the application of several modern multi-objective evolutionary algorithms to the design and training of an ANN for the classification of samples from two different biomedical datasets. Numerical results show that different algorithms have different strengths and weaknesses, leading to ANNs characterized by different levels of classification accuracy and network complexity.

关键词： Artificial Neural Networks multi-objective evolutionary algorithms Akaike Information Criterion

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Fuzzy Classification with multi-objective evolutionary algorithms

Fuzzy Classification with Multi-objective Evolutionary Algor...

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3rd International Workshop on Hybrid Artificial Intelligence Systems

作者： Jimenez, Fernando Sanchez, Gracia Sanchez, Jose F. Alcaraz, Jose M. Fac Informat Campus Espinardo Murcia 30071 Spain

ISBN: (纸本)9783540876557

In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.

关键词： multi-objective evolutionary algorithms Fuzzy classification

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evolutionary Method for Weight Vector Generation in multi-objective evolutionary algorithms based on Decomposition and Aggregation

Evolutionary Method for Weight Vector Generation in Multi-Ob...

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IEEE Congress on evolutionary Computation (CEC)

作者： Meneghini, Ivan Reinaldo Guimaraes, Frederico Gadelha Univ Fed Minas Gerais Grad Program Elect Engn Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Elect Engn Machine Intelligence & Data Sci Lab MINDS Belo Horizonte MG Brazil

ISBN: (纸本)9781509046010

The generation of weight vectors is the primary step in MOEA based on decomposition and aggregation methods, affecting the diversity of the Pareto approximation and overall performance of the algorithm. The basic methods, following the method proposed by Scheffe, have some limitations mainly when the number of objectives increases, because the number of weight vectors and hence the population size becomes very large. In this paper, we present a new method for weight vector generation that can create an arbitrary number of weight vectors, almost equally spaced, located in a surface in the first orthant of the objective space, with free choice of norm. The proposed evolutionary algorithm is able to prevent the creation of weight vectors along the border of the orthant, which is a region that contains solutions of little interest to the decision maker. With a small modification in the proposed method it is also possible to create cones of weight vectors, useful to explore specific regions of the decision space defined by preference directions. In our experiments, different sets of weight vectors were generated, varying the number of vectors and the dimension of the space. The validation of the results was given by the mean distance of each vector to its nearest neighbor, as well as the standard deviation and the Pearson coefficient of variation for this mean value. The results indicate that the proposed method is able to produce a distribution of vectors close to a uniform distribution, with no clustering of points, being useful for guiding decomposition-based MOEA.

关键词： Weight Vectors Generation multi-objective evolutionary algorithms MOEA/D Decomposition Mixture Experiments

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A Framework of Quantum-inspired multi-objective evolutionary algorithms and its Convergence Condition 07

A Framework of Quantum-inspired Multi-Objective Evolutionary...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Li, Zhiyong Rudolph, Guenter Hunan Univ Sch Comp & Commun Changsha 410082 Hunan Peoples R China Univ Dortmund Fachbereich Informat Lehrstuhl Algorithm Engn D-44221 Dortmund Germany

A general framework of quantum-inspired multi-objective evolutionary algorithms as well as one of its sufficient convergence conditions to Pareto optimal set is proposed.

ISBN: (纸本)9781595936974

A general framework of quantum-inspired multi-objective evolutionary algorithms as well as one of its sufficient convergence conditions to Pareto optimal set is proposed.

关键词： Quantum computing multi-objective evolutionary algorithms Pareto optimal set stochastic convergence

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Maximizing Submodular or Monotone Functions Under Partition Matroid Constraints by multi-objective evolutionary algorithms 16th

Maximizing Submodular or Monotone Functions Under Partition ...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Anh Viet Do Neumann, Frank Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9783030581145;9783030581152

Many important problems can be regarded as maximizing submodular functions under some constraints. A simple multi-objective evolutionary algorithm called GSEMO has been shown to achieve good approximation for submodular functions efficiently. While there have been many studies on the subject, most of existing run-time analyses for GSEMO assume a single cardinality constraint. In this work, we extend the theoretical results to partition matroid constraints which generalize cardinality constraints, and show that GSEMO can generally guarantee good approximation performance within polynomial expected run time. Furthermore, we conducted experimental comparison against a baseline GREEDY algorithm in maximizing undirected graph cuts on random graphs, under various partition matroid constraints. The results show GSEMO tends to outperform GREEDY in quadratic run time.

关键词： evolutionary algorithms multi-objective evolutionary algorithms Run-time analysis

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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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