By Giovanni Squillero, Paolo Burelli
The volumes LNCS 9597 and 9598 represent the refereed convention court cases of the nineteenth eu convention at the purposes of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 occasions EuroGP, EvoCOP, and EvoMUSART.
The fifty seven revised complete papers offered including 17 poster papers have been conscientiously reviewed and chosen from one hundred fifteen submissions. EvoApplications 2016 consisted of the subsequent thirteen tracks: EvoBAFIN (natural computing tools in company analytics and finance), EvoBIO (evolutionary computation, desktop studying and information mining in computational biology), EvoCOMNET (nature-inspired concepts for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in photograph research, sign processing, and trend recognition), EvoINDUSTRY (nature-inspired concepts in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for probability administration, defense and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
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Extra resources for Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II
Example text
Further analyses have shown that an archive size of N = 500 is an appropriate choice. Fig. 2. Comparison of meta-model size N = 20 and N = 500 on the Sphere function with (a) d = 2 and (b) d = 10. Our analysis of the neighborhood size k have shown that the choice k = 1 yields the best results in all cases. Larger choices slow down the optimization or let the optimization process stagnate, similar to the stagnation we observe for small archive sizes. Hence, we understand the nearest neighbor regression meta-models with k = 1 as local meta-models, which also belong to the most successful ones in literature, see Sect.
From Table 3. , its benefit increases with dimensionality. Secondly, the GDO-9 configuration performs best out of the 9 different parameterisations, when considered across all problem dimensions. Table 3. Number of functions (out of 13) where a GDO configuration achieves better quality solutions than GDO-free Config. Dimension Dimension [Opt-aiNET] [SawTooth] 2 10 30 2 10 30 GDO-1 5 8 12 7 7 11 GDO-2 8 8 11 9 8 12 GDO-3 6 8 11 9 9 8 GDO-4 5 6 10 6 10 11 GDO-5 8 7 10 5 10 9 GDO-6 9 8 10 8 9 9 GDO-7 8 5 12 7 9 10 GDO-8 9 6 11 7 5 10 GDO-9 10 9 12 9 9 10 Since the algorithm/configuration that optimises both objectives (minimises convergence speed and minimises summed fitness) is always considered more successful, additionally we provide graphs that summarise the results concerning both solution quality and convergence speed.
Solving timetabling problems using a cultural algorithm. Appl. Soft Comput. 11(1), 337–344 (2011) 2. : Grid diversity operator for some population-based optimization algorithms. In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1475–1476. ACM (2015) 26 A. Salah et al. 3. : Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1996) ˇ 4. : Exploration and exploitation in evolutionary algorithms. ACM Comput.
