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Heuristics for Optimization and Learning

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Heuristics for Optimization and Learning/ edited by Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi.
other author:	Yalaoui, Farouk.
Description:	XV, 442 p. 168 illus., 97 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-58930-1
ISBN:	9783030589301

Heuristics for Optimization and Learning
Heuristics for Optimization and Learning[electronic resource] /edited by Farouk Yalaoui, Lionel Amodeo, El-Ghazali Talbi. - 1st ed. 2021. - XV, 442 p. 168 illus., 97 illus. in color.online resource. - Studies in Computational Intelligence,9061860-9503 ;. - Studies in Computational Intelligence,564.

Process Plan Generation for Reconﬁgurable Manufacturing Systems: Exact vs Evolutionary-Based Multi-Objective Approaches -- On VNS-GRASP and Iterated Greedy Metaheuristics for Solving Hybrid Flow Shop Scheduling Problem with Uniform Parallel Machines and Sequence Independent Setup Time -- A Variable Block Insertion Heuristic for the Energy-Efﬁcient Permutation Flowshop Scheduling with Makespan Criterion -- Solving 0-1 Bi-Objective Multi-Dimensional Knapsack Problems using Binary Genetic Algorithm -- An asynchronous parallel evolutionary algorithm for solving large .instances of the multi-objective QAP -- Learning from Prior Designs for Facility Layout Optimization -- Single-objective Real-parameter Optimization: Enhanced LSHADE-SPACMA Algorithm -- Operations Research at Bulk Terminal: A Parallel Column Generation Approach -- Heuristic solutions for the (α,β)-k feature set problem -- Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization -- Dynamic Simulated Annealing with Adaptive Neighborhood using Hidden Markov Model -- Hybridization of the differential evolution algorithm for continuous multi-objective optimization -- A Steganographic Embedding Scheme Using Improved-PSO Approach -- Algorithms towards the Automated Customer Inquiry Classiﬁcation -- An heuristic scheme for a reaction advection diffusion equation -- Stock Market Speculation System Development based on Technico Temporal indicators and Data Mining Tools -- A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System -- A new cut-based genetic algorithm for graph partitioning applied to cell formation -- Memetic algorithm and evolutionary operators for multi-objective matrix tri-factorization problem -- Quaternion simulated annealing -- A Cooperative Multi-Swarm Particle Swarm Optimizer Based Hidden Markov Model -- Experimental Sensitivity Analysis of Grid-Based Parameter Adaptation Method -- Auto-Scaling System in Apache Spark Cluster using Model-Based Deep Reinforcement Learning -- Innovation Networks from Inter-Organizational Research Collaborations -- Assessing Film Coefﬁcients of Microchannel Heat Sinks via Cuckoo Search Algorithm -- One-Class Subject Authentication using Feature Extraction by Grammatical Evolution on Accelerometer Data -- Semantic composition of word-embeddings with genetic programming -- New Approach for Continuous and Discrete Optimization: Optimization by Morphological Filters.

This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.

ISBN: 9783030589301

Standard No.: 10.1007/978-3-030-58930-1doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q342

Dewey Class. No.: 006.3

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