Central Library

Normal view MARC view ISBD view

Evolutionary Computation in Combinatorial Optimization [electronic resource] : 24th European Conference, EvoCOP 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3-5, 2024, Proceedings / edited by Thomas Stützle, Markus Wagner.

Contributor(s): Stützle, Thomas [editor.] | Wagner, Markus [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 14632Publisher: Cham : Springer Nature Switzerland : Imprint: Springer, 2024Edition: 1st ed. 2024.Description: XI, 194 p. 41 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031577123.Subject(s): Computer science -- Mathematics | Computer science | Computer networks  | Artificial intelligence | Mathematics of Computing | Theory of Computation | Computer Communication Networks | Artificial IntelligenceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 004.0151 Online resources: Click here to access online
Contents:
A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem -- Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem -- Emergence of new local search algorithms with neuro-evolution -- Q-learning Based Framework for Solving the Stochastic E-waste Collection Problem -- A memetic algorithm with adaptive operator selection for graph coloring -- Studies on Multi-objective Role Mining in ERP Systems -- Greedy heuristic guided by lexicographic excellence -- Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination -- Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances -- Hardest Monotone Functions for Evolutionary Algorithms -- A Theoretical Investigation Of Termination Criteria For Evolutionary Algorithms -- Experimental and Theoretical Analysis of Local Search Optimising OBDD Variable Orderings.
In: Springer Nature eBookSummary: This book constitutes the referred proceedings of the 24th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2024, held as part of EvoStar 2024, in Aberystwyth, UK, during April 3-5, 2024. The 12 full papers presented in this book were carefully reviewed and selected from 28 submissions. They cover a variety of topics, ranging from constructive algorithms, machine learning techniques ranging from neural network based guidance to sparse surrogate models for optimization problems, the foundation of evolutionary computation algorithms and other search heuristics, to multi-objective optimization problems. .
    average rating: 0.0 (0 votes)
No physical items for this record

A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem -- Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem -- Emergence of new local search algorithms with neuro-evolution -- Q-learning Based Framework for Solving the Stochastic E-waste Collection Problem -- A memetic algorithm with adaptive operator selection for graph coloring -- Studies on Multi-objective Role Mining in ERP Systems -- Greedy heuristic guided by lexicographic excellence -- Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination -- Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances -- Hardest Monotone Functions for Evolutionary Algorithms -- A Theoretical Investigation Of Termination Criteria For Evolutionary Algorithms -- Experimental and Theoretical Analysis of Local Search Optimising OBDD Variable Orderings.

This book constitutes the referred proceedings of the 24th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2024, held as part of EvoStar 2024, in Aberystwyth, UK, during April 3-5, 2024. The 12 full papers presented in this book were carefully reviewed and selected from 28 submissions. They cover a variety of topics, ranging from constructive algorithms, machine learning techniques ranging from neural network based guidance to sparse surrogate models for optimization problems, the foundation of evolutionary computation algorithms and other search heuristics, to multi-objective optimization problems. .

There are no comments for this item.

Log in to your account to post a comment.