Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Introduction to algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Using Experimental Design to Find Effective Parameter Settings for Heuristics
Journal of Heuristics
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
MALLBA: A Library of Skeletons for Combinatorial Optimisation (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
iOpt: A Software Toolkit for Heuristic Search Methods
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Programming Challenges: The Programming Contest Training Manual
Programming Challenges: The Programming Contest Training Manual
Localizer++: An Open Library for local Search
Localizer++: An Open Library for local Search
The Rational Unified Process: An Introduction
The Rational Unified Process: An Introduction
Genetic Programming and Evolvable Machines
Ant Colony Optimization
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Parallel Combinatorial Optimization (Wiley Series on Parallel and Distributed Computing)
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Design and Analysis of Experiments
Design and Analysis of Experiments
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
Tuning Metaheuristics: A Machine Learning Perspective
Tuning Metaheuristics: A Machine Learning Perspective
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A taxonomy of cooperative search algorithms
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
International Journal of Applied Metaheuristic Computing
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Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors' knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.