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Computers and Operations Research - Special issue: Applications of integer programming
Introduction to graph theory
Combinatorial optimization: algorithms and complexity
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Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Artificial intelligence (3rd ed.)
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An introduction to genetic algorithms
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Principles of Program Analysis
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
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GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
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AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Ant Colony Optimization
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Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
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This paper is concerned with taking an engineering approach towards the application of metaheuristic problem solving methods, i.e., heuristics that aim to solve a wide variety of problems. How can a practitioner solve a problem using metaheuristic methods? What choices do they have, and how are these choices influenced by the problem at hand? Are there sensible universal choices which can be made, or are these choices always problem-dependent? The aim of this paper is to address questions such as these in the context of a (soft) engineering design framework for the application of metaheuristics. The aim of this framework is to make explicit the choices which a practitioner needs to make in applying these techniques, and to give some guidelines for how metaheuristics might be tuned to problems by considering different problem- and solution-types.