Evolutionary Multi-Criterion Optimization

1. Aspects of Algorithm Design

   1. Difficulty in Evolutionary Multiobjective Optimization of Discrete Objective Functions with Different Granularities

      • Hisao Ishibuchi, Masakazu Yamane, Yusuke Nojima

      Pages 230-245

   2. Solution of Multi-objective Min-Max and Max-Min Games by Evolution

      • Gideon Avigad, Erella Eisenstadt, Valery Y. Glizer

      Pages 246-260

   3. A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization

      • Miqing Li, Shengxiang Yang, Xiaohui Liu, Ruimin Shen

      Pages 261-275

2. Pareto-Based Methods

   1. Effect of Dominance Balance in Many-Objective Optimization

      • Kaname Narukawa

      Pages 276-290

   2. An Alternative Preference Relation to Deal with Many-Objective Optimization Problems

      • Antonio L’opez, Carlos A. Coello Coello, Akira Oyama, Kozo Fujii

      Pages 291-306

   3. An Improved Adaptive Approach for Elitist Nondominated Sorting Genetic Algorithm for Many-Objective Optimization

      • Himanshu Jain, Kalyanmoy Deb

      Pages 307-321

   4. Adaptive ε-Sampling and ε-Hood for Evolutionary Many-Objective Optimization

      • Hernán Aguirre, Akira Oyama, Kiyoshi Tanaka

      Pages 322-336

3. Hybrid MCDA

   1. ‘‘Whatever Works Best for You’’- A New Method for a Priori and Progressive Multi-objective Optimisation

      • Rui Wang, Robin C. Purshouse, Peter J. Fleming

      Pages 337-351

   2. Hypervolume-Based Multi-Objective Reinforcement Learning

      • Kristof Van Moffaert, Madalina M. Drugan, Ann Nowé

      Pages 352-366

   3. A Theoretical Analysis of Curvature Based Preference Models

      • Pradyumn Kumar Shukla, Michael Emmerich, André Deutz

      Pages 367-382

4. Decomposition-Based Methods

   1. Force-Based Cooperative Search Directions in Evolutionary Multi-objective Optimization

      • Bilel Derbel, Dimo Brockhoff, Arnaud Liefooghe

      Pages 383-397

   2. Approximation Model Guided Selection for Evolutionary Multiobjective Optimization

      • Aimin Zhou, Qingfu Zhang, Guixu Zhang

      Pages 398-412

   3. A Decomposition Based Evolutionary Algorithm for Many Objective Optimization with Systematic Sampling and Adaptive Epsilon Control

      • Md. Asafuddoula, Tapabrata Ray, Ruhul Sarker

      Pages 413-427

   4. Generalized Decomposition

      • Ioannis Giagkiozis, Robin C. Purshouse, Peter J. Fleming

      Pages 428-442

   5. Evenly Spaced Pareto Front Approximations for Tricriteria Problems Based on Triangulation

      • Günter Rudolph, Heike Trautmann, Soumyadip Sengupta, Oliver Schütze

      Pages 443-458

   6. Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems

      • Hisao Ishibuchi, Naoya Akedo, Yusuke Nojima

      Pages 459-474

5. Classical MCDA

   1. Multiple Criteria Hierarchy Process for the Choquet Integral

      • Silvia Angilella, Salvatore Corrente, Salvatore Greco, Roman Słowiński

      Pages 475-489

   2. Selection of Inspection Intervals Based on Multi-attribute Utility Theory

      • Rodrigo J. P. Ferreira, Adiel T. de Almeida, Cristiano A. V. Cavalcante

      Pages 490-499

   3. Minimizing the Compensatory Effect of MCDM Group Decision Additive Aggregation Using the Veto Concept

      • Suzana de França Dantas Daher, Adiel Teixeira de Almeida

      Pages 500-512

6. Exploratory Problem Analysis

   1. A Dimensionally-Aware Genetic Programming Architecture for Automated Innovization

      • Sunith Bandaru, Kalyanmoy Deb

      Pages 513-527

This book constitutes the refereed proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 held in Sheffield, UK, in March 2013. The 57 revised full papers presented were carefully reviewed and selected from 98 submissions. The papers are grouped in topical sections on plenary talks; new horizons; indicator-based methods; aspects of algorithm design; pareto-based methods; hybrid MCDA; decomposition-based methods; classical MCDA; exploratory problem analysis; product and process applications; aerospace and automotive applications; further real-world applications; and under-explored challenges.

• evolutionary algorithms
• multi-objective optimization
• multiple criteria decision making
• preference models
• uncertainty
• algorithm analysis and problem complexity

• Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

  Robin C. Purshouse

• Department of Automatic Control Systems and Engineering, University of Sheffield, Sheffield, UK

  Peter J. Fleming

• Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

  Carlos M. Fonseca

• Department of Economics and Business, University of Catania, Catania, Italy

  Salvatore Greco

• Unilever Research & Development Port Sunlight, Merseyside, UK

  Jane Shaw

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