Evolutionary Multi-Criterion Optimization

This book constitutes the refereed proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009, held in Nantes, France in April 2009. The 39 revised full papers presented together with 5 invited talks were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on theoretical analysis, uncertainty and noise, algorithm development, performance analysis and comparison, applications, MCDM Track, Many objectives, alternative methods, as well as EMO and MCDA.

[1]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[2]  Shengxiang Yang,et al.  A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization , 2013, EMO.

[3]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[4]  K. Fang,et al.  A new approach to construction of nearly uniform designs , 2004 .

[5]  Li Mo,et al.  The RM-MEDA Based on Elitist Strategy , 2010, ISICA.

[6]  Lothar Thiele,et al.  The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration , 2007, EMO.

[7]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[8]  Christian Igel,et al.  Improved step size adaptation for the MO-CMA-ES , 2010, GECCO '10.

[9]  Fang Liu,et al.  MOEA/D with Adaptive Weight Adjustment , 2014, Evolutionary Computation.

[10]  Gabriele Eichfelder,et al.  Adaptive Scalarization Methods in Multiobjective Optimization , 2008, Vector Optimization.

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[12]  Hong Li,et al.  MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives , 2013, Comput. Oper. Res..

[13]  Yong Wang,et al.  A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant cluster operator , 2012, Appl. Soft Comput..

[14]  Kiyoshi Tanaka,et al.  A Review of Features and Limitations of Existing Scalable Multiobjective Test Suites , 2019, IEEE Transactions on Evolutionary Computation.

[15]  Dipti Srinivasan,et al.  A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition , 2017, IEEE Transactions on Evolutionary Computation.

[16]  Saúl Zapotecas Martínez,et al.  LIBEA: A Lebesgue Indicator-Based Evolutionary Algorithm for multi-objective optimization , 2019, Swarm Evol. Comput..

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[18]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[19]  Qingfu Zhang,et al.  A model-based evolutionary algorithm for bi-objective optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[20]  Kai-Tai Fang,et al.  A note on construction of nearly uniform designs with large number of runs , 2003 .

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[22]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[23]  Y. Wang,et al.  An Historical Overview of Lattice Point Sets , 2002 .

[24]  Kai-Tai Fang,et al.  Admissibility and minimaxity of the uniform design measure in nonparametric regression model , 2000 .

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[26]  Saúl Zapotecas Martínez,et al.  On the low-discrepancy sequences and their use in MOEA/D for high-dimensional objective spaces , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[27]  M. Peruggia.  Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data , 2003 .

[28]  Nicola Beume,et al.  Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization , 2007, EMO.

[29]  Lei Chen,et al.  A Fast Approximate Hypervolume Calculation Method by a Novel Decomposition Strategy , 2017, ICIC.

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