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7th International Conference on Engineering Computational Technology

作者： Marquez, A. L. Gil, C. Manzano-Agugliaro, F. Montoya, F. G. Montoya, M. G. Banos, R. Univ Almeria Dept Comp Architecture & Elect La Canada De San Urbano Almeria Spain Univ Almeria Dept Rural Engn La Canada De San Urbano Almeria Spain

ISBN: (纸本)9781905088416

multi-objective evolutionary algorithms are commonly used to solve complex problems. It is possible to improve the quality of the solutions they can produce by means of employing advanced parallelization techniques. This paper solves a land management problem by using the Island Parallel Model with the purpose of expanding the number of objectives to optimize by using several isolated populations exploring two dimensional search spaces and migrating the best subjects to nearby populations that are exploring a search space with a different objective. The goal is to fuel the generation of solutions that are optimal in both search spaces to generate a three objective pareto front that is the solution of a larger problem.

关键词： multi-objective evolutionary algorithms island parallel model threads greenhouse crops resource optimization

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multi-objective Robust Static Mapping of Independent Tasks on Grids

Multi-objective Robust Static Mapping of Independent Tasks o...

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2010 IEEE World Congress on Computational Intelligence

作者： Dorronsoro, Bernabe Bouvry, Pascal Canero, J. Alberto Maciejewski, Anthony A. Siegel, Howard Jay Univ Luxembourg Fac Sci & Commun Luxembourg Luxembourg Colorado State Univ Dept ECE Ft Collins CO USA Colorado State Univ Dept CS Ft Collins CO 80523 USA

ISBN: (纸本)9781424481262

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms.

关键词： evolutionary computation grid computing grid system independent tasks multi-objective evolutionary algorithms multi-objective robust static mapping task analysis

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Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2009年第7期50卷 1066-1080页

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

In this paper we propose a multi-objective evolutionary algorithm to generate Mamdani fuzzy rule-based systems with different good trade-offs between complexity and accuracy. The main novelty of the algorithm is that both rule base and granularity of the uniform partitions defined on the input and output variables are learned concurrently. To this aim, we introduce the concepts of virtual and concrete rule bases: the former is defined on linguistic variables, all partitioned with a fixed maximum number of fuzzy sets, while the latter takes into account, for each variable, a number of fuzzy sets as determined by the specific partition granularity of that variable. We exploit a chromosome composed of two parts, which codify the variables partition granularities, and the virtual rule base, respectively. Genetic operators manage virtual rule bases, whereas fitness evaluation relies on an appropriate mapping strategy between virtual and concrete rule bases. The algorithm has been tested on two real-world regression problems showing very promising results. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Mamdani fuzzy rule-based systems multi-objective evolutionary algorithms Partition granularity learning Accuracy-interpretability trade-off

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A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 8255-8268页

作者： Lau, H. C. W. Chan, T. M. Tsui, W. T. Chan, F. T. S. Ho, G. T. S. Choy, K. L. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Hong Kong Peoples R China

In the field Of Supply chain management and logistics, using vehicles to deliver products from suppliers to customers is one of the major operations. Before transporting products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. This paper deals with the problem of optimization of vehicle routing in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time constraint, the objective considered in this paper comprises not only the total traveling distance, but also the total traveling time. We propose using a multi-objective evolutionary algorithm called the fuzzy logic guided non-dominated sorting genetic algorithm 2 (FL-NSGA2) to solve this multi-objective optimization problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FL-NSGA2, we compared it with the following: non-dominated sorting genetic algorithms 2 (NSGA2) (without the guide of fuzzy logic), strength Pareto evolutionary algorithm 2 (SPEA2) (with and without the guide of fuzzy logic), and micro-genetic algorithm (MICROGA) (with and without the guide of fuzzy logic). Simulation results showed that FL-NSGA2 outperformed other search methods in all of three various scenarios. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Supply chain management Logistics Vehicle routing problem multi-objective optimization multi-objective evolutionary algorithms Genetic algorithms Fuzzy logic

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Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

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SOFT COMPUTING 2009年第5期13卷 437-449页

作者： Botta, Alessio Lazzerini, Beatrice Marcelloni, Francesco Stefanescu, Dan C. IMT Lucca Inst Adv Studies I-55100 Lucca Italy Univ Pisa Dipartimento Ingn Infromaz I-56122 Pisa Italy Suffolk Univ Dept Math & Comp Sci Boston MA USA

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.

关键词： Fuzzy rule-based systems Context adaptation multi-objective evolutionary algorithms Fuzzy partition interpretability

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multi-objective evolutionary learning of granularity, membership function parameters and rules of Mamdani fuzzy systems

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evolutionary INTELLIGENCE 2009年第1-2期2卷 21-37页

作者： Antonelli, Michela Ducange, Pietro Lazzerini, Beatrice Marcelloni, Francesco Univ Pisa Dipartimento Ingn Informaz Elett Informat Telecom Via Diotisalvi 2 I-56122 Pisa Italy

In this paper, we propose a multi-objective evolutionary algorithm (MOEA) to generate Mamdani fuzzy rule-based systems with different trade-offs between accuracy and complexity by learning concurrently granularities of the input and output partitions, membership function (MF) parameters and rules. To this aim, we introduce the concept of virtual and concrete partitions: the former is defined by uniformly partitioning each linguistic variable with a fixed maximum number of fuzzy sets;the latter takes into account, for each variable, the number of fuzzy sets determined by the evolutionary process. Rule bases and MF parameters are defined on the virtual partitions and, whenever a fitness evaluation is required, mapped to the concrete partitions by employing appropriate mapping strategies. The implementation of the MOEA relies on a chromosome composed of three parts, which codify the partition granularities, the virtual rule base and the membership function parameters, respectively, and on purposely-defined genetic operators. The MOEA has been tested on three real-world regression problems achieving very promising results. In particular, we highlight how starting from randomly generated solutions, the MOEA is able to determine different granularities for different variables achieving good trade-offs between complexity and accuracy.

关键词： Accuracy-interpretability trade-off Granularity learning Mamdani fuzzy rule-basedsystems multi-objective evolutionary algorithms Piecewise linear transformation

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A multi-objective evolutionary algorithm for protein structure prediction with immune operators

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2009年第4期12卷 407-413页

作者： Judy, M. V. Ravichandran, K. S. Murugesan, K. SASTRA Univ Sch Comp Thanjavur India Natl Inst Technol Dept Math Tiruchirappalli India

Genetic algorithms (GA) are often well suited for optimisation problems involving several conflicting objectives. It is more suitable to model the protein structure prediction problem as a multi-objective optimisation problem since the potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms) and experiments have shown that those types of interactions are in conflict, by using the potential energy function, Chemistry at Harvard Macromolecular Mechanics. In this paper, we have modified the immune inspired Pareto archived evolutionary strategy (I-PAES) algorithm and denoted it as MI-PAES. It can effectively exploit some prior knowledge about the hydrophobic interactions, which is one of the most important driving forces in protein folding to make vaccines. The proposed MI-PAES is comparable with other evolutionary algorithms proposed in literature, both in terms of best solution found and the computational time and often results in much better search ability than that of the canonical GA.

关键词： protein structure prediction multi-objective evolutionary algorithms genetic algorithms immune operators Pareto archived evolutionary strategy

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Non-dominated multi-objective evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery

Non-dominated Multi-objective Evolutionary Algorithm Based o...

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4th International Workshop on Hybrid Artificial Intelligence Systems

作者： Carmona, C. J. Gonzalez, P. del Jesus, M. J. Herrera, F. Univ Jaen Dept Comp Sci Jaen Spain AI Univ Granada Dept Comp Sci Granada Spain

ISBN: (纸本)9783642023187

A new multi-objective evolutionary model for Subgroup discovery with fuzzy rules is presented in this paper. The method resolves Subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the population. This proposal focuses on the classification of individuals in fronts, based on non-dominated sort. A study can be seen for the proposal and other previous methods for different databases. In this study good results are obtained for subgroup discovery by this new evolutionary model in comparison with existing algorithms.

关键词： Data mining Subgroup Discovery multi-objective evolutionary algorithms Fuzzy Rules Genetic Fuzzy Systems

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Scheduling for the National Hockey League Using a multi-objective evolutionary Algorithm

Scheduling for the National Hockey League Using a Multi-obje...

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22nd Australian Joint Conference on Artificial Intelligence

作者： Craig, Sam While, Lyndon Barone, Luigi Univ Western Australia Nedlands WA 6009 Australia

ISBN: (纸本)9783642104381

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

关键词： Sports scheduling 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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