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Evolutionary Multi-Criterion Optimization de António Gaspar-Cunha

Evolutionary Multi-Criterion Optimization: Lecture Notes in Computer Science, cartea 9018

Editat de António Gaspar-Cunha, Carlos Henggeler Antunes, Carlos Coello Coello
en Limba Engleză Paperback – 19 mar 2015
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.
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Specificații

ISBN-13: 9783319159331
ISBN-10: 331915933X
Pagini: 472
Ilustrații: XXIV, 447 p. 156 illus.
Dimensiuni: 155 x 235 x 26 mm
Greutate: 0.71 kg
Ediția:2015
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

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.