Journal ArticleDOI
A framework for the description of evolutionary algorithms
Alain Hertz,Daniel Kobler +1 more
Reads0
Chats0
TLDR
A terminology and a general framework is given for the description of the main features of any particular evolutionary algorithm, to develop tools that may help understanding the “philosophy” of such methods.About:
This article is published in European Journal of Operational Research.The article was published on 2000-10-01. It has received 121 citations till now. The article focuses on the topics: Evolutionary programming & Evolutionary music.read more
Citations
More filters
Journal ArticleDOI
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
Christian Blum,Andrea Roli +1 more
TL;DR: A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
Journal ArticleDOI
A survey on metaheuristics for stochastic combinatorial optimization
TL;DR: In this paper metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others are introduced, and their applications to the class of Stochastic Combinatorial Optimization Problems (SCOPs) is thoroughly reviewed.
Book
Parallel Metaheuristics: A New Class of Algorithms
TL;DR: This chapter discusses Metaheuristics and Parallelism in Telecommunications, which has applications in Bioinformatics and Parallel Meta heuristics, and Theory of Parallel Genetic Algorithms, which focuses on the latter.
Journal ArticleDOI
A recommender system using GA K-means clustering in an online shopping market
Kyoung-jae Kim,Hyunchul Ahn +1 more
TL;DR: The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms and validated the usefulness of the proposed model as a preprocessing tool for recommendation systems.
BookDOI
Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Stefan Voß,David L. Woodruff +1 more
TL;DR: Optimization Modeling starts with an mrp Model and extends to an MRP II Model, a Better Model and Extensions to the Model, and Implementation Examples.
References
More filters
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.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Book
Handbook of Genetic Algorithms
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
Book ChapterDOI
Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey
TL;DR: In this article, the authors survey the state of the art with respect to optimization and approximation algorithms and interpret these in terms of computational complexity theory, and indicate some problems for future research and include a selective bibliography.