By Giovanni Squillero, Paolo Burelli
The volumes LNCS 9597 and 9598 represent the refereed convention lawsuits 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 rigorously reviewed and chosen from a hundred and fifteen submissions. EvoApplications 2016 consisted of the subsequent thirteen tracks: EvoBAFIN (natural computing equipment in company analytics and finance), EvoBIO (evolutionary computation, computing device studying and information mining in computational biology), EvoCOMNET (nature-inspired thoughts for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and intricate systems), EvoENERGY (evolutionary computation in power applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in snapshot research, sign processing, and development recognition), EvoINDUSTRY (nature-inspired thoughts in commercial settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for possibility administration, protection and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).
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Extra info for Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II
In addition, the statistical analysis conducted in this paper conﬁrmed GDO-9’s superiority. However, by incorporating feedback from search process it seems clear that this could be achieved autonomously, so that both parameters in fact could adapt over a single run to achieve better performance. We aim to address this issue in future work. References 1. : 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.
Springer, Heidelberg (2006) 13. : On the diversity mechanisms of opt-aiNet: a comparative study with ﬁtness sharing. In: IEEE World Congress on Computational Intelligence, WCCI 2010–2010 IEEE Congress on Evolutionary Computation, CEC 2010 (2010) 14. : A Saw-tooth genetic algorithm combining the eﬀects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–28 (2006) 15. : Problem deﬁnitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization.
We can observe that the ES with meta-model leads to a signiﬁcant reduction of ﬁtness function evaluations. For d = 2, the standard deviation even falls below a value that is measurable due to a limited machine accuracy. The results are statistically signiﬁcant, which is conﬁrmed by the Wilcoxon test. On Rosenbrock, we set a = 50. The MM-(1+1)ES signiﬁcantly outperforms the (1+1)-ES for d = 2, but only in few runs on Rosenbrock leading to no statistical superiority for d = 10. Table 1. Experimental comparison of (1+1)-ES and the MM-(1+1)-ES on the Sphere function and on Rosenbrock.
Applications of Evolutionary Computation: 19th European Conference, EvoApplications 2016, Porto, Portugal, March 30 -- April 1, 2016, Proceedings, Part II by Giovanni Squillero, Paolo Burelli