Evolutionary Multi-Criterion Optimization

8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I

Häftad, Engelska, 2015

Av António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello, Antonio Gaspar-Cunha

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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 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.

Produktinformation

Utgivningsdatum2015-03-19
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieTheoretical Computer Science and General Issues
Antal sidor447
FörlagSpringer International Publishing AG
ISBN9783319159331

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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 adaptivescalarising 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.