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Band 15513

Evolutionary Multi-Criterion Optimization 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part II

Aus der Reihe Lecture Notes in Computer Science

58,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.02.2025

Abbildungen

XVII, 266 p. 91 illus., 88 illus. in color.

Herausgeber

Hemant Singh + weitere

Verlag

Springer Singapore

Seitenzahl

266

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9635-37-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

28.02.2025

Abbildungen

XVII, 266 p. 91 illus., 88 illus. in color.

Herausgeber

Verlag

Springer Singapore

Seitenzahl

266

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

435 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-981-9635-37-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Evolutionary Multi-Criterion Optimization
  • Produktbild: Evolutionary Multi-Criterion Optimization

  • .- Algorithm analysis.


    .- Visual Explanations of Some Problematic Search Behaviors of Frequently Used EMO Algorithms.


    .- Numerical Analysis of Pareto Set Modeling.


    .- When Is Non-deteriorating Population Update in MOEAs Beneficial?.


    .- Analysis of Merge Non-dominated Sorting Algorithm.


    .- Comparative Analysis of Indicators for Multi-objective Diversity Optimization.


    .- Performance Analysis of Constrained Evolutionary Multi-Objective Optimization Algorithms on Artificial and Real-World Problems.


    .- On the Approximation of the Entire Pareto Front of a Constrained Multi objective Optimization Problem.


    .- Small Population Size is Enough in Many Cases with External Archives.


    .- Surrogates and machine learning.


    .- Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations.


    .- Surrogate Strategies for Scalarisation-based Multi-objective Bayesian Optimizers.


    .- A Mixed-Fidelity Evaluation Algorithm for Efficient Constrained Multi- and Many-Objective Optimization: First Results.


    .- Efficient and Accurate Surrogate-Assisted Approach to Multi-Objective Optimization Using Deep Neural Networks.


    .- Large Language Model for Multiobjective Evolutionary Optimization.


    .- Multi-Objective Multi-Agent Reinforcement Learning for Autonomous Driving in Mixed-Traffic Environments.


    .- Parallel TD3 for Policy Gradient-based Multi-Condition Multi-Objective Optimisation.


    .- Multi-criteria decision support.


    .- Reliability-based MCDM Using Objective Preferences Under Variable Uncertainty.


    .- An Efficient Iterative Approach for Uniformly Representing Pareto Fronts.


    .- Preference Learning for Multi-objective Reinforcement Learning by Means of Supervised Learning.


    .- Bayesian preference elicitation for decision support in multi-objective optimization.