By Giovanni Squillero, Paolo Burelli
The volumes LNCS 9597 and 9598 represent the refereed convention court cases 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 one hundred fifteen submissions. EvoApplications 2016 consisted of the next thirteen tracks: EvoBAFIN (natural computing tools in company analytics and finance), EvoBIO (evolutionary computation, computing device studying and information mining in computational biology), EvoCOMNET (nature-inspired ideas 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 photo research, sign processing, and development recognition), EvoINDUSTRY (nature-inspired options in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for hazard administration, protection 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 I
Input sequences are serially processed using a single-step rolling window. 3 Scope of Research The literature review conﬁrms that the task of sequence classiﬁcation by means of GP with memory has received very limited attention [8,18,28,30]. We hypothesise that the use of state variables in sequence learning problems is hindered when a ﬁxed window of past sequence values is used as input. In such cases the search quickly converges to areas of the space containing autoregressive models, and ignores the use of memory.
2 presents scatter plots of test ADR versus the number of feedback loops in all 2, 500 best-of-generation individuals accumulated during 50 evolutionary runs of 50 generations each. Our aim is to qualify the relationship between generalisation performance and memory utilisation. Figure 2(g) shows that in the case of S&P500 a number of feedback loops greater than 3 is mainly seen with positive test-set ADR. The Pearson correlation coeﬃcient (PCC) of ADR vs. 25, suggesting a weak linear relationship between the two.
Agapitos et al.  (2008) evolved classiﬁers that use indexed memory for EEG signal classiﬁcation. Agapitos et al.  (2011) used indexed memory to represent models of racing tracks during the evolution of car-racing controllers. Finally, Agapitos et al.  (2011) used GP with indexed memory to evolve decision-trees for ﬁnancial time-series classiﬁcation. 3 Recurrent GP Another form of memory in GP may be implemented via the notion of recurrency in terms of time-delay feedback loops. In sequence processing, time-delay feedback loops refer to a type of program input in which program outputs issued at time t − 1 are used as part of the input used in program execution at time t.