Evolutionary Multi-Criterion Optimization: Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, ProceedingsMulticriterion optimization refers to problems with two or more objectives (n- mally in con?ict with each other) which must be simultaneously satis?ed. Multicriterion optimization problems have not one but a set of solutions (which represent trade-o's among the objectives), which are called Pareto optimal - lutions. Thus, the main goal in multicriterion optimization is to ?nd or to - proximate the set of Pareto optimal solutions. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing popularity over the last 10 years. The 3rd International Conference on Evolutionary Multi-criterion Optimi- tion(EMO2005)washeldduringMarch9-11,2005, inGuanajuato, M ́ exico.This wasthethirdinternationalconferencededicatedentirelytothisimportanttopic, followingthesuccessfulEMO2001andEMO2003conferences, whichwereheldin Z] urich, SwitzerlandinMarch2001, andinFaro, PortugalinApril2003, respectively. The EMO 2005 scienti?c program included two keynote addresses, one given by Peter Fleming on an engineering design perspective of many-objective op- mization, and the other given by Milan Zeleny on the evolution of optimality. In addition, three tutorials were presented, one on metaheuristics for multiobj- tivecombinatorialoptimizationbyXavierGandibleux, anotheronmultiobjective evolutionary algorithms by Gary B. Lamont, and a third one on performance assessment of multiobjective evolutionary algorithms by Joshua D. Knowles. |
Contents
Invited Talks | 1 |
An Engineering Design Perspective | 14 |
Tutorial | 33 |
Algorithm Improvements | 47 |
An EMO Algorithm Using the Hypervolume Measure as Selection | 62 |
The Combative Accretion Model Multiobjective Optimisation | 77 |
Parallelization of Multiobjective Evolutionary Algorithms Using | 92 |
OMOEAII | 108 |
Clonal Selection with Immune Dominance and Anergy Based | 474 |
A Multiobjective Tabu Search Algorithm for Constrained Optimisation | 490 |
Improving PSOBased Multiobjective Optimization Using Crowding | 505 |
Differential Evolution for Multiobjective Optimization | 520 |
Multiobjective Model Selection for Support Vector Machines | 534 |
Exploiting the TradeOff The Benefits of Multiple Objectives | 547 |
Extraction of Design Characteristics of Multiobjective Optimization | 561 |
Application | 576 |
Path Relinking in Pareto Multiobjective Genetic Algorithms | 120 |
Dynamic Archive Evolution Strategy for Multiobjective | 135 |
Searching for Robust ParetoOptimal Solutions in Multiobjective | 150 |
Multiobjective MaxiMin Sorting Scheme | 165 |
Initial Population Construction for Convergence Improvement | 191 |
A Preliminary Study | 206 |
Incorporation of Preferences | 221 |
A Multiobjective Evolutionary Algorithm for Deriving Final Ranking | 235 |
Performance Analysis and Comparison | 250 |
Recombination of Similar Parents in EMO Algorithms | 265 |
A Scalable Multiobjective Test Problem Toolkit | 280 |
Extended Multiobjective fast messy Genetic Algorithm Solving | 296 |
Comparing Classical Generating Methods with an Evolutionary | 311 |
A New Analysis of the LebMeasure Algorithm for Calculating | 326 |
Effects of Removing Overlapping Solutions on the Performance of | 341 |
Selection Drift Recombination and Mutation in Multiobjective | 355 |
Comparison Between Lamarckian and Baldwinian Repair | 370 |
A Performance Comparison | 386 |
Uncertainty and Noise | 399 |
Multiobjective Optimization of Problems with Epistemic Uncertainty | 413 |
Alternative Methods | 428 |
New Ideas in Applying Scatter Search to Multiobjective Optimization | 443 |
A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts | 459 |
Multiobjective Genetic Algorithms to Create Ensemble of Classifiers | 592 |
Multiobjective Model Optimization for Inferring Gene Regulatory | 607 |
HighFidelity Multidisciplinary Design Optimization of Wing Shape | 621 |
Photonic Device Design Using Multiobjective Evolutionary Algorithms | 636 |
Multiple Criteria LotSizing in a Foundry Using Evolutionary | 651 |
Multiobjective Shape Optimization Using Estimation Distribution | 664 |
A Multiobjective Approach to Integrated Risk Management | 692 |
An Approach Based on the Strength Pareto Evolutionary Algorithm 2 | 707 |
Proposition of Selection Operation in a Genetic Algorithm for a | 721 |
A TwoLevel Evolutionary Approach to Multicriterion Optimization | 736 |
Evolutionary Multiobjective Optimization for Simultaneous Generation | 752 |
A Multiobjective Memetic Algorithm for Intelligent Feature Extraction | 767 |
Solving the Aircraft Engine Maintenance Scheduling Problem Using | 782 |
Finding ParetoOptimal Set by Merging Attractors for a Biobjective | 797 |
Multiobjective EA Approach for Improved Quality of Solutions | 811 |
Developments on a Multiobjective Metaheuristic MOMH Algorithm | 826 |
Preliminary Investigation of the Learnable Evolution Model | 841 |
Particle Evolutionary Swarm for Design Reliability Optimization | 856 |
Multiobjective Water Pinch Analysis of the Cuernavaca City Water | 870 |
Multiobjective Vehicle Routing Problems Using TwoFold | 885 |
| 911 | |
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Common terms and phrases
applied approach approximation archive average better calculated clustering Coello combination compared comparison complexity components Computation considered constraints convergence corresponding cost crossover decision defined described different distance distribution diversity dominated effective Engineering evaluations Evolutionary Algorithms Evolutionary Computation example experiments Figure first function Genetic Algorithms given global IEEE improvement increase indicator individuals initial maximum mean measure method metric minimization MOEA multiobjective optimization multiple mutation non-dominated Note NSGA-II objective objective function objective space obtained operator optimisation optimization problems parameters parent Pareto front Pareto-optimal performance points population presented probability procedure proposed random ranking reference relation represents respect risk robust rules runs scheme selection shown shows similar single solutions solving space SPEA2 standard Step strategy structure Table test problems tion values variables vector weight