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  • Produktbild: Evolutionary Multi-Criterion Optimization
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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

Fr. 84.90

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.