By Giovanni Squillero, Paolo Burelli

The volumes LNCS 9597 and 9598 represent the refereed convention complaints 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 provided 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, laptop studying and information mining in computational biology), EvoCOMNET (nature-inspired thoughts 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 options in business settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for threat 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 (Lecture Notes in Computer Science)

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Genetic Programming (GP) [1] has been widely applied to sequence learning tasks. The vast majority of systems employ a sliding time-window of size W , where at each time-step t, values {st−1 , . . , st−W } from a sequence {si }N i=1 are used to populate an input vector of explanatory variables used to predict sequence value st . This input vector configuration biases towards the evolution of some sort of an autoregressive model. Stateful Genetic Programming (GP) deals with the evolution of computer programs that utilise memory.

We observe that on average Genetic Programming with Memory For Financial Trading 31 the best-of-generation individuals contain at least one feedback loop, and that in the case of S&P500 the average feedback loops is set to 2. The second row of Fig. 2 shows the evolution of the percentage of stateful programs in the population. A stateful program is one that contains at least one of the memory manipulation primitives write, softAdd or softMul. First we note that the initial proportion of stateful individuals is consistently set to approximately 20 % in all problems.

Finally, individuals outside the limits are ranked ranging from 3 to pop size according to the distance from the valid interval. The clustering is done using the current 1 In probability theory, the expected value, usually denoted by E[X], refers to the value of a random variable X that we would “expect” to find out if we could repeat the random variable process an infinite number of times and take the average of the values obtained. C. Cortes and A. Rau-Chaplin Input: NG = number of generations; nbest = number of best individuals; slice size=discretization; Estimate the min and the max of the mean; Divide the interval [min, max] into n slabs; foreach slab do while (NG not reached) do Create the population using probability matrix; Use the mean to determine the risk value; if (mean of one individual is outside the chunk) then risk value = 0; else risk value = compute(mean) end Merge archive and the new population; Determine the non-dominated set; Cluster the non-dominated set into k clusters; Select k representative individuals; Select worst individuals; Insert the k individuals into the best population; Identify the best and worst buckets; Update and mutate the probability matrix; end end Combine the results of each slab; Determine the Pareto frontier; Algorithm 1.

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Applications of Evolutionary Computation: 19th European by Giovanni Squillero, Paolo Burelli
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