Evolutionary Multi-Criterion Optimization 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I /

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015  held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Gaspar-Cunha, António (Editor ), Henggeler Antunes, Carlos (Editor ), Coello, Carlos Coello (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Theoretical Computer Science and General Issues, 9018
Temas:
Numerical analysis.
Algorithms.
Computer science.
Artificial intelligence.
Application software.
Numerical Analysis.
Theory of Computation.
Artificial Intelligence.
Computer and Information Systems Applications.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Plenary Talks
  • Interactive Approaches in Multiple Criteria Decision Making and Evolutionary Multi-objective Optimization
  • Towards Automatically Configured Multi-objective Optimizers
  • A Review of Evolutionary Multiobjective Optimization Applications in Aerospace Engineering
  • Performance evaluation of multiobjective optimization algorithms: quality indicators and the attainment function
  • Theory and Hyper-Heuristics
  • A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results
  • Unwanted Feature Interactions Between the Problem and Search Operators in Evolutionary Multi-objective Optimization
  • Neutral but a Winner! How Neutrality helps Multiobjective Local Search Algorithms
  • To DE or not to DE? Multi-Objective Differential Evolution Revisited from a Component-Wise Perspective
  • Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
  • Temporal Innovization: Evolution of Design Principles Using Multi-objective  Optimization
  • MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems
  • Using hyper-heuristic to select leader and archiving methods for many-objective problems
  • Algorithms
  • Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts
  • MOEA/PC: Multiobjective Evolutionary Algorithm Based on Polar Coordinates
  • GD-MOEA: A New Multi-Objective Evolutionary Algorithm based on the Generational Distance Indicator
  • Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming
  • A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment
  • A Knee-based EMO Algorithm with an Efficient Method to Update Mobile Reference Points
  • A Hybrid Algorithm for Stochastic Multiobjective Programming Problem
  • Parameter Tuning of MOEAs using a Bilevel Optimization Approach
  • Pareto adaptive scalarising functions for decomposition based algorithms
  • A bi-level multiobjective PSO algorithm
  • An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems
  • Combining Non-dominance, Objective-sorted and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
  • GACO: a parallel evolutionary approach to multi-objective scheduling
  • Kriging Surrogate Model Enhanced by Coordinate Transformation of Design Space Based on Eigenvalue Decomposition
  • A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem
  • Evolutionary Inference of Attribute-based Access Control Policies
  • Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
  • A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem
  • Comparing Decomposition-based and Automatically Component-Wise Designed Multi-objective Evolutionary Algorithms
  • Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
  • Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions.

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