Evolutionary Multi-Criterion Optimization

1. Front Matter

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2. Invited Talks

   1. Aspiration Level Methods in Interactive Multi-objective Programming and Their Engineering Applications (Abstract of Invited Talk)

      • Hirotaka Nakayama

      Pages 1-1

   2. Improving the Efficacy of Multi-objective Evolutionary Algorithms for Real-World Applications (Abstract of Invited Talk)

      • Kay Chen Tan

      Pages 2-2

   3. Decision Making in Evolutionary Optimization (Abstract of Invited Talk)

      • Carlos M. Fonseca

      Pages 3-3

   4. MOEAs in the Design of Network Centric Systems (Abstract of Invited Talk)

      • Gary B. Lamont

      Pages 4-4

3. Algorithm Design

   1. Controlling Dominance Area of Solutions and Its Impact on the Performance of MOEAs

      • Hiroyuki Sato, Hernán E. Aguirre, Kiyoshi Tanaka

      Pages 5-20

   2. Designing Multi-objective Variation Operators Using a Predator-Prey Approach

      • Christian Grimme, Joachim Lepping

      Pages 21-35

   3. Capabilities of EMOA to Detect and Preserve Equivalent Pareto Subsets

      • Günter Rudolph, Boris Naujoks, Mike Preuss

      Pages 36-50

   4. Optimization of Scalarizing Functions Through Evolutionary Multiobjective Optimization

      • Hisao Ishibuchi, Yusuke Nojima

      Pages 51-65

   5. Reliability-Based Multi-objective Optimization Using Evolutionary Algorithms

      • Kalyanmoy Deb, Dhanesh Padmanabhan, Sulabh Gupta, Abhishek Kumar Mall

      Pages 66-80

   6. Multiobjective Evolutionary Algorithms on Complex Networks

      • Michael Kirley, Robert Stewart

      Pages 81-95

   7. On Gradient Based Local Search Methods in Unconstrained Evolutionary Multi-objective Optimization

      • Pradyumn Kumar Shukla

      Pages 96-110

4. Algorithm Improvements

   1. Symbolic Archive Representation for a Fast Nondominance Test

      • Martin Lukasiewycz, Michael Glaß, Christian Haubelt, Jürgen Teich

      Pages 111-125

   2. Design Issues in a Multiobjective Cellular Genetic Algorithm

      • Antonio J. Nebro, Juan J. Durillo, Francisco Luna, Bernabé Dorronsoro, Enrique Alba

      Pages 126-140

   3. FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems

      • Hamidreza Eskandari, Christopher D. Geiger, Gary B. Lamont

      Pages 141-155

   4. Constraint-Handling Method for Multi-objective Function Optimization: Pareto Descent Repair Operator

      • Ken Harada, Jun Sakuma, Isao Ono, Shigenobu Kobayashi

      Pages 156-170

   5. Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES

      • Christian Igel, Thorsten Suttorp, Nikolaus Hansen

      Pages 171-185

   6. A Multi-tiered Memetic Multiobjective Evolutionary Algorithm for the Design of Quantum Cascade Lasers

      • Mark P. Kleeman, Gary B. Lamont, Adam Cooney, Thomas R. Nelson

      Pages 186-200

   7. Local Search in Two-Fold EMO Algorithm to Enhance Solution Similarity for Multi-objective Vehicle Routing Problems

      • Tadahiko Murata, Ryota Itai

      Pages 201-215

   8. Mechanism of Multi-Objective Genetic Algorithm for Maintaining the Solution Diversity Using Neural Network

      • Kenji Kobayashi, Tomoyuki Hiroyasu, Mitsunori Miki

      Pages 216-226

Multicriterion optimization refers to problems with two or more objectives (normally in conflict with each other) which must be simultaneously satisfied. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing attention from industry. The 4th International Conference on Evolutionary Multi-criterion Optimization (EMO2007) was held during March 5–8, 2007, in Matsushima/Sendai, Japan. This was the fourth international conference dedicated entirely to this important topic, following the successful EMO 2001, EMO 2003 and EMO 2005 conferences, which were held in Zürich, Switzerland in March 2001, in Faro, Portugal in April 2003, and in Guanajuato, México in March 2005. EMO2007 was hosted by the Institute of Fluid Science, Tohoku University. EMO2007 was co-hosted by the Graduate School of Information Sciences, Tohoku University, the Japan Aerospace Exploration Agency (JAXA), and the Policy Grid Computing Laboratory, Kansai University. The EMO2007 scientific program included four keynote speakers: Hirotaka Nakayama on aspiration level methods, Kay Chen Tan on large and computationally intensive real-world MO optimization problems, Carlos Fonseca on decision making, and Gary B. Lamont on design of large-scale network centric systems.

• adaptive search
• algorithm design
• algorithmics
• algorithms
• approximation
• automata
• classification
• constraint solving
• evolutionary algorithm
• genetic programming
• heuristics
• metaheuristics
• optimization
• programming
• visualization
• algorithm analysis and problem complexity

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