By Per Kristian Lehre, Carsten Witt (auth.), Luca Di Gaspero, Andrea Schaerf, Thomas Stützle (eds.)
Metaheuristics were a truly lively examine subject for greater than twenty years. in this time many new metaheuristic recommendations were devised, they've been experimentally established and more advantageous on difficult benchmark difficulties, and so they have confirmed to be vital instruments for tackling optimization projects in various sensible functions. In different phrases, metaheuristics are these days confirmed as one of many major seek paradigms for tackling computationally not easy difficulties. nonetheless, there are lots of study demanding situations within the quarter of metaheuristics. those demanding situations diversity from extra primary questions about theoretical houses and function promises, empirical set of rules research, the potent configuration of metaheuristic algorithms, methods to mix metaheuristics with different algorithmic ideas, in the direction of extending the to be had recommendations to take on ever more difficult problems.
This edited quantity grew out of the contributions awarded on the 9th Metaheuristics overseas 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 through 169 delegates. The chapters which are amassed during this publication exemplify contributions to numerous of the examine instructions defined above.
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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.
Table 5 presents (1) the original ranking, (2) the ranking obtained by removing p01 , p02 , p04 , and p09 , and (3) the ranking resulting from removing only p17 . Recall that, in all cases, test-problems p20 and p22 have been excluded from the suite. 2 0 1 10 log(τ) 100 Fig. C. Barbosa, Heder S. S. 2 0 1 10 log(τ) 100 Fig. 9: Performance profiles for the results of the CEC 2006 competition when the test-problem p17 is excluded Table 5: Comparison between the original ranking (with respect to the AUC) and the ranking obtained when different test-problems are removed from the suite 1 Standard s01 AUC from Fig.
4: Performance profiles for the results of the CEC’06 competition weighted by the dimension of the problems Using Performance Profiles for the Analysis and Design of Benchmark Experiments 29 problems can be excluded a priori from the analysis. The corresponding standard performance profiles are presented in Fig. 3. The performance profiles when the test-problems are weighted according to problem dimensionality (Eq. 3) and hardness (Eq. 5) are shown in Figs. 4 and 5, respectively. Table 2 shows the rank induced by the area under the curve considering the standard performance profile as well as its two weighted variants.