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Quantum-inspired evolutionary algorithms: a survey and empirical study

by  Gexiang Zhang


 Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.


How to initiate students in the art of metaheuristics???

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Dear metaheuristic colleagues,

It took me a while to find out how to get master and PhD students started to develop metaheuristics. Then, I found a very good book to give them: Metaheuristics - From Design to Implementation written by El-Ghazali Talbi (published by Wiley in 2009).

In my opinion, this book presents a very good introduction to the art of metaheuristics and how to design them. The master and PhD students I gave the book, all agree (as they should Smile ). Obviously some chapters might not be useful for all students, but to get the general idea and start developing metaheuristics it really works well

Maybe some of you have other hints to share with your metaheuristic colleagues? Other books, articles, presentations, tutorials, etc.? Feel free to add those to this topic.

Kind regards,

Pieter Vansteenwegen


Computers & Operations Research

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A metaheuristic for a teaching assistant assignment-routing problem

by Pablo Maya, Kenneth Sörensen and Peter Goos


The Flemish Ministry of Education promotes the integrated education of disabled children by providing educational opportunities in common schools. In the current system, disabled children receive ambulant help from a teaching assistant (TA) employed at an institute for extra-ordinary education. The compensation that the TAs receive for driving to visit the pupils is a major cost factor for the institute that provides the assistance. Therefore, the institute's management desires a schedule that minimizes the accumulated distance traveled by all TAs combined. We call this optimization problem the teaching assistants assignment-routing problem (TAARP). It involves three decisions that have to be taken simultaneously: (1) pupils have to be assigned to TAs; (2) pupils assigned to a given TA have to be spread over the TA's different working days; and (3) the order in which to visit the pupils on each day has to be determined. We propose a solution strategy based on an auction algorithm and a variable neighborhood search heuristic which has an excellent performance when applied to both simulated and real instances. The total distance traveled in the solution obtained for the institute's data set improves the current solution by about 22% which represents a saving of approximately 9% on the annual budget of the institute for integrated education.

Last Updated on Sunday, 05 June 2011 09:47

Addison-Wesley Professional

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Genetic Algorithms in Search, Optimization, and Machine Learning

by David E. Goldberg


David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.

Last Updated on Friday, 27 May 2011 11:48

The MIT Press

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Adaptation in Natural and Artificial Systems

An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

by John H. Holland

Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.

Last Updated on Friday, 27 May 2011 11:40

Online book

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Essentials of Metaheuristics

by Sean Luke

About the book

This is an open set of lecture notes on metaheuristics algorithms, intended for undergraduate students, practitioners, programmers, and other non-experts. It was developed as a series of lecture notes for an undergraduate course I taught at GMU. The chapters are designed to be printable separately if necessary. As it's lecture notes, the topics are short and light on examples and theory. It's best when complementing other texts. With time, I might remedy this.

Last Updated on Friday, 25 April 2014 06:25

ORSA Journal on Computing

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Tabu Search—Part II

by Fred Glover


This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overcome the limitations of local optimality both in a deterministic and a probabilistic framework. Part I also reported successful applications from a wide range of settings, in which tabu search frequently made it possible to obtain higher quality solutions than previously obtained with competing strategies, generally with less computational effort. Part II, in this issue, examines refinements and more advanced aspects of tabu search. Following a brief review of notation, Part II introduces new dynamic strategies for managing tabu lists, allowing fuller exploitation of underlying evaluation functions. In turn, the elements of staged search and structured move sets are characterized, which bear on the issue of finiteness. Three ways of applying tabu search to the solution of integer programming problems are then described, providing connections also to certain nonlinear programming applications. Finally, the paper concludes with a brief survey of new applications of tabu search that have occurred since the developments reported in Part I. Together with additional comparisons with other methods on a wide body of problems, these include results of parallel processing implementations and the use of tabu search in settings ranging from telecommunications to neural networks.

Last Updated on Friday, 27 May 2011 11:55

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