By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)
Metaheuristics were a really energetic learn subject for greater than 20 years. in this time many new metaheuristic options were devised, they've been experimentally verified and greater on not easy benchmark difficulties, they usually have confirmed to be very important instruments for tackling optimization initiatives in various useful purposes. In different phrases, metaheuristics are these days verified as one of many major seek paradigms for tackling computationally demanding difficulties. nonetheless, there are quite a few study demanding situations within the zone of metaheuristics. those demanding situations variety from extra primary questions about theoretical homes and function promises, empirical set of rules research, the powerful configuration of metaheuristic algorithms, methods to mix metaheuristics with different algorithmic thoughts, in the direction of extending the to be had options to take on ever tougher problems.
This edited quantity grew out of the contributions provided on the 9th Metaheuristics foreign convention that used to be held in Udine, Italy, 25-28 July 2011. The convention comprised 117 displays of peer-reviewed contributions and three invited talks, and it's been attended via 169 delegates. The chapters which are accrued during this publication exemplify contributions to a number of of the study instructions defined above.
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9 s06 2 s02 s02 s07 3 s03 s03 s01 4 s04 s04 s02 5 s05 s05 s03 6 s06 s06 s05 7 s07 s07 s04 8 s08 s08 s08 9 s09 s09 s09 10 s10 s10 s10 5 Conclusions Benchmark experiments play a fundamental role in the design, analysis, and evaluation of algorithms in general and of optimization metaheuristics in particular. In this paper we discussed performance profiles, which are tools for analyzing the results of such experiments. We argued that, due to their explanatory power, such profiles should be more widely used by the metaheuristics community.
The ideal best solver (s j = s j∗ ) would be the best performer in every test-problem in P, that is, r(pi , s j∗ ) = 1 for all i. However, this is very hard to be accomplished in a large set of representative problems. If solver s1 is the best in a subset of problems from P while s2 is the best in the remaining problems, how to decide that one is better than the other? To exemplify, and to allow for a simple graphical representation, one could think of a hypothetical benchmark situation with only two test-problems (|P| = 2) and four solvers, as sketched in Fig.
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 . Examples of model-free algorithm configuration methods are F-Race  and Iterated F-Race , ParamILS  and genetic algorithm GGA ; examples of model-based approaches are SPO+  and SMAC .
Advances in Metaheuristics by Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)