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

709 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2015-03-19
Mått155 x 235 x 26 mm
Vikt709 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor447
FörlagSpringer International Publishing AG
ISBN9783319159331

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Programmeringsböcker inom Data och it
Artificiell intelligens inom Data och it
Människa – datorinteraktion inom Data och it
Beräkning och matematisk analys inom Naturvetenskap och teknik
Tillämpad datateknik inom Data och it

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.