By Giovanni Squillero, Paolo Burelli
The volumes LNCS 9597 and 9598 represent the refereed convention complaints of the nineteenth eu convention at the functions 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 a hundred and fifteen submissions. EvoApplications 2016 consisted of the subsequent thirteen tracks: EvoBAFIN (natural computing tools in company analytics and finance), EvoBIO (evolutionary computation, computer studying and information mining in computational biology), EvoCOMNET (nature-inspired recommendations for telecommunication networks and different parallel and allotted systems), EvoCOMPLEX (evolutionary algorithms and intricate systems), EvoENERGY (evolutionary computation in power applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in photograph research, sign processing, and development recognition), EvoINDUSTRY (nature-inspired ideas in commercial settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for threat administration, safety and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
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Additional info for Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II
2 State of the Art In fact, the examination of the running time of an evolutionary algorithm has received some attention from early on. Implementation matters [6,7], which implies that paying attention to the particular way an algorithm is implemented might result in speed improvements that outclass those achieved by using the a priori fastest language available. In fact, careful coding and choosing the right tools [8,9] in interpreted languages can make them as fast, or even faster, than compiled languages such as Java.
Statistical Analysis Although the graphical summaries given above provide some insight and intuition regarding GDO performance, a proper statistical analysis is required to justify any claims. The statistical analysis in this section addresses the following questions: – Does GDO injection helps improve solution quality when compared to the equivalent GDO-free algorithm? – Which GDO conﬁguration (from the proposed nine conﬁgurations) shows the best performance in terms of solution quality? Since we have two algorithms, each with 10 possible conﬁgurations (9 GDO, 1 GDO-free) and each is tested against 13 problems in each of three diﬀerent dimensions, there are 780 datasets.
However, all the GDO conﬁgurations use more evaluation cycles than the GDO-free version. For the tendimensional tests, Fig. 3 shows that 7 out of 9 of the GDO conﬁgurations of optainet — all but GDO-7 and GDO-8 — surpasses the GDO-free version in terms of solution quality. In this case, GDO-4,GDO-5, and GDO-8 conﬁgurations used less number of function evaluations than the GDO-free version while GDO-1, GDO-3, GDO-6 were similar to GDO-free and the rest were the worst. Figure 4 Validating the Grid Diversity Operator Fig.