By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)

ISBN-10: 1461463211

ISBN-13: 9781461463214

ISBN-10: 146146322X

ISBN-13: 9781461463221

Metaheuristics were a truly energetic learn subject for greater than 20 years. in this time many new metaheuristic recommendations were devised, they've been experimentally demonstrated and superior on not easy benchmark difficulties, and so they have confirmed to be very important instruments for tackling optimization initiatives in various useful purposes. In different phrases, metaheuristics are these days proven as one of many major seek paradigms for tackling computationally difficult difficulties. nonetheless, there are a great number of study demanding situations within the quarter of metaheuristics. those demanding situations diversity from extra basic questions about theoretical homes and function promises, empirical set of rules research, the powerful configuration of metaheuristic algorithms, ways to mix metaheuristics with different algorithmic options, in the direction of extending the on hand concepts to take on ever tougher problems.

This edited quantity grew out of the contributions awarded on the 9th Metaheuristics overseas convention that was once held in Udine, Italy, 25-28 July 2011. The convention comprised 117 shows of peer-reviewed contributions and three invited talks, and it's been attended by means of 169 delegates. The chapters which are amassed during this e-book exemplify contributions to a number of of the examine instructions defined above.

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751–758. ACM (2010) 5. : Experimental research in evolutionary computation – the new experimentalism. In: Natural Computing Series. Springer, Berlin (2006) 6. , Preuss, M. ): Experimental Methods for the Analysis of Optimization Algorithms. Springer, Berlin (2010) 7. : Multi-Objective Optimization Using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, Chichester (2001) 8. : Benchmarcking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002) 9.

In: Proceedings of Foundations of Genetic Algorithms 10 (FOGA’09), pp. 13–20. C. Barbosa, Heder S. S. Barreto Abstract It is common to manipulate a large amount of data generated in the benchmarking process when comparing metaheuristics. Performance profiles are analytical tools for the visualization and interpretation of these results. Here we comment on their explanatory power, discuss novel variants, introduce a multicriterion view of the performance comparison, and also define performance profiles for each test-problem in a given benchmark suite.

These approaches can be classified into model-free algorithm configuration methods and model-based approaches. Model-free algorithms are simpler to implement than model-based approaches because the former can be applied out-of-the-box, while the latter requires iterations between fitting models and using them to make choices about which configurations to investigate [13]. Examples of model-free algorithm configuration methods are F-Race [4] and Iterated F-Race [5], ParamILS [10] and genetic algorithm GGA [2]; examples of model-based approaches are SPO+ [12] and SMAC [13].

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Advances in Metaheuristics by Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)


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