Journal ArticleDOI
Using a hybrid of exact and genetic algorithms to design survivable networks
Elham Ghashghai,Ronald L. Rardin +1 more
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A hybrid method for finding approximate optimal solutions for survivable network problems on complete graphs that takes advantage of k -tree solvability and combines efficient algorithms for special cases with a randomized search through a sequence of such cases until a satisfactory design is obtained.About:
This article is published in Computers & Operations Research.The article was published on 2002-01-01. It has received 19 citations till now. The article focuses on the topics: Node (networking) & Tree (graph theory).read more
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Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Ronald L. Rardin,Reha Uzsoy +1 more
TL;DR: The methodological issues that must be confronted by researchers undertaking experimental evaluations of heuristics, including experimental design, sources of test instances, measures of algorithmic performance, analysis of results, and presentation in papers and talks are highlighted.
Journal ArticleDOI
Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach
TL;DR: Computational results show that the genetic algorithm developed to solve multiprocessor task scheduling in a multistage hybrid flow-shop environment is both effective and efficient for the current problem.
Journal ArticleDOI
Scheduling jobs on a k -stage flexible flow-shop
Carlos D. Paternina-Arboleda,Jairo R. Montoya-Torres,Milton J. Acero-Dominguez,Maria C. Herrera-Hernandez +3 more
TL;DR: In this article, a heuristic algorithm based on the identification and exploitation of the bottleneck stage was proposed for makespan minimization on a flexible flow shop with k stages and ms machines at any stage.
Journal ArticleDOI
P-hub protection models for survivable hub network design
TL;DR: Results reveal that PROBA, the model with a back-up routing scheme, considerably enhances the network resilience and even the network performance, indicating that the model is a candidate for a strong survivable hub network design.
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Constraint ordinal optimization
David Li,Loo Hay Lee,Yu-Chi Ho +2 more
TL;DR: A new framework and quantification methods for dealing with the constraint optimization problem is built, which is called the framework constraint ordinal optimization.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Genetic Algorithms and Random Keys for Sequencing and Optimization
TL;DR: A general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem is presented.
Journal ArticleDOI
Complexity of finding embeddings in a k -tree
TL;DR: This work determines the complexity status of two problems related to finding the smallest number k such that a given graph is a partial k-tree and presents an algorithm with polynomially bounded (but exponential in k) worst case time complexity.