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54
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 294 (16 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Exploring Hyperheuristic Methodologies with Genetic Programming
"... Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are n ..."
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Cited by 36 (13 self)
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Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most metaheuristics implementations. In the majority of hyperheuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyperheuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process
Reactive Search, a historybased heuristic for MAXSAT
 ACM Journal of Experimental Algorithmics
, 1996
"... The Reactive Search (RS) method proposes the integration of a simple historybased feedback scheme into local search for the online determination of free parameters. In this paper a new RS algorithm is proposed for the approximated solution of the Maximum Satisfiability problem: a component base ..."
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Cited by 27 (1 self)
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The Reactive Search (RS) method proposes the integration of a simple historybased feedback scheme into local search for the online determination of free parameters. In this paper a new RS algorithm is proposed for the approximated solution of the Maximum Satisfiability problem: a component based on local search with temporary prohibitions is complemented with a reactive scheme that determines ("learns") the appropriate value of the prohibition parameter by monitoring the Hamming distance along the search trajectory (algorithm HRTS). In addition, the nonoblivious functions recently introduced in the framework of approximation algorithms are used to discover a better local optimum in the initial part of the search.
Applications of Modern Heuristic Search Methods to Pattern Sequencing Problems
 COMPUTERS & OPERATIONS RESEARCH
, 1999
"... This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultane ..."
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Cited by 24 (6 self)
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This article describes applications of modern heuristic search methods to pattern sequencing problems, i.e., problems seeking for a permutation of the rows of a given matrix with respect to some given objective function. We consider two di#erent objectives: Minimization of the number of simultaneously open stacks and minimization of the average order spread. Both objectives require the adaptive evaluation of changed solutions to allow an e#cient application of neighbourhood search techniques.
Metaheuristics: The state of the art
 LOCAL SEARCH FOR PLANNING AND SCHEDULING
"... Metaheuristics support managers in decisionmaking with robust tools that provide highquality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of metaheuristics. This prima ..."
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Cited by 20 (2 self)
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Metaheuristics support managers in decisionmaking with robust tools that provide highquality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of metaheuristics. This primarily focuses on the significant progress which general frames within the metaheuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.
Locating Hidden Groups in Communication Networks Using Hidden Markov Models
"... A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoauge its communications amongst the typical communications of the network. We study the ta ..."
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Cited by 17 (4 self)
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A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoauge its communications amongst the typical communications of the network. We study the task of detecting such hidden groups given only the history of the communications for the entire communication network. We develop a probabilistic approach using a Hidden Markov model of the communication network. Our approach does not require the use of any semantic information regarding the communications.
Solving the Continuous FlowShop Scheduling Problem Metaheuristics
 European Journal of Operational Research
, 2001
"... this paper, we discuss, from a practical point of view, the e#ectiveness of applying reusable metaheuristics software components to the continuous flowshop scheduling problem. This includes analyzing the knowledge and e#orts needed to adapt the metaheuristics and analyzing by experiments the trade ..."
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Cited by 13 (4 self)
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this paper, we discuss, from a practical point of view, the e#ectiveness of applying reusable metaheuristics software components to the continuous flowshop scheduling problem. This includes analyzing the knowledge and e#orts needed to adapt the metaheuristics and analyzing by experiments the tradeo# between running time and solution quality. Our goal is to gain general insights in the e#ectiveness of applying di#erent types of metaheuristics with respect to di#erent demands for solution quality and di#erent amounts of available resources such as knowledge about algorithms, implementation e#orts and running time. In Section 2, we first describe the continuous flowshop scheduling problem. Then, in Sections 3 and 4, we review di#erent kinds of construction methods and metaheuristics. The implementation is briefly discussed in Section 5. In Section 6, we provide and discuss extensive computational results. Finally, we draw some conclusions and give directions for future research
A.J.: Mapping the performance of heuristics for constraint satisfaction
 In: IEEE Congress on Evolutionary Computation (CEC
, 2010
"... Abstract — Hyperheuristics are high level search methodologies that operate over a set of heuristics which operate directly on the problem domain. In one of the hyperheuristic frameworks, the goal is automating the process of selecting a humandesigned low level heuristic at each step to construc ..."
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Cited by 10 (10 self)
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Abstract — Hyperheuristics are high level search methodologies that operate over a set of heuristics which operate directly on the problem domain. In one of the hyperheuristic frameworks, the goal is automating the process of selecting a humandesigned low level heuristic at each step to construct a solution for a given problem. Constraint Satisfaction Problems (CSP) are well know NP complete problems. In this study, behaviours of two variable ordering heuristics MaxConflicts (MXC) and Saturation Degree (SD) with respect to various combinations of constraint density and tightness values are investigated in depth over a set of random CSP instances. The empirical results show that the performance of these two heuristics are somewhat complementary and they vary for changing constraint density and tightness value pairs. The outcome is used to design three hyperheuristics using MXC and SD as low level heuristics to construct a solution for unseen CSP instances. It has been observed that these hyperheuristics improve the performance of individual low level heuristics even further in terms of mean consistency checks for some CSP instances. I.
DomainIndependent Local Search For Linear Integer Optimization
, 1998
"... Integer and combinatorial optimization problems constitute a major challenge for algorithmics. They arise when a large number of discrete organizational decisions have to be made, subject to constraints and optimization criteria. This thesis ..."
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Cited by 10 (1 self)
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Integer and combinatorial optimization problems constitute a major challenge for algorithmics. They arise when a large number of discrete organizational decisions have to be made, subject to constraints and optimization criteria. This thesis