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

By Giovanni Squillero, Paolo Burelli

The volumes LNCS 9597 and 9598 represent the refereed convention lawsuits of the nineteenth ecu 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 provided including 17 poster papers have been conscientiously reviewed and chosen from one hundred fifteen submissions. EvoApplications 2016 consisted of the next thirteen tracks: EvoBAFIN (natural computing equipment in enterprise analytics and finance), EvoBIO (evolutionary computation, computing device studying and information mining in computational biology), EvoCOMNET (nature-inspired innovations 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 photo 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, safeguard 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

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27–41, 2016. J. Merelo et al. Let us emphasize that running speed is not everything. In many cases, ease of integration with existing or legacy tools, coding speed or availability of parallel or distributed frameworks are much more important than how fast a single CPU program runs. There are also restrictions inherent to the application at hand, such as embedded or web based systems where certain languages must be used. However, in some cases, mainly when the size of the problem or the running time of the algorithm call for the maximum running speed available, it is interesting at least to know which languages can be used to obtain the best performance.

A guided anisotropic search governs the search direction based on the history of the BSP tree which results in an individual being either exploitatively mutated towards the nearest historical optimum or randomly mutated if the individual is already a local optimum. Other learning methods [10] use self-organising maps (SOMs) to learn about unexplored areas of the space using information from the whole evolutionary history; this information is used to determine novelty and thus encourage exploration.

Squillero and P. ): EvoApplications 2016, Part II, LNCS 9598, pp. 11–26, 2016. 1007/978-3-319-31153-1 2 12 A. Salah et al. The term diversity refers to differences among individuals which can be at either the genotype or phenotype level. Typically, diversity is classified into three categories: diversity maintenance, diversity control, and diversity learning. Diversity maintenance encourages exploring multiple promising pathways through the search space simultaneously in hopes of reaching higher fitness peaks.

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