Applications of Evolutionary Computation: EvoApplications by José Manuel Lanza-Gutiérrez, Juan Antonio Gómez-Pulido,

By José Manuel Lanza-Gutiérrez, Juan Antonio Gómez-Pulido, Miguel A. Vega-Rodríguez (auth.), Cecilia Di Chio, Alexandros Agapitos, Stefano Cagnoni, Carlos Cotta, Francisco Fernández de Vega, Gianni A. Di Caro, Rolf Drechsler, Anikó Ekárt, Anna I. Esparcia-Al

This booklet constitutes the refereed lawsuits of the foreign convention at the functions of Evolutionary Computation, EvoApplications 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 occasions EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The fifty four revised complete papers provided have been rigorously reviewed and chosen from ninety submissions. EvoApplications 2012 consisted of the subsequent eleven tracks: EvoCOMNET (nature-inspired suggestions for telecommunication networks and different parrallel and allotted systems), EvoCOMPLEX (algorithms and intricate systems), EvoFIN (evolutionary and usual computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoHOT (bio-inspired heuristics for layout automation), EvoIASP (evolutionary computation in photo research and sign processing), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for hazard administration, safeguard and safety applications), EvoSTIM (nature-inspired innovations in scheduling, making plans, and timetabling), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

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Extra info for Applications of Evolutionary Computation: EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Málaga, Spain, April 11-13, 2012, Proceedings

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A) (b) (c) Fig. 4. Fitness as a function of the x and y position of the first input of a random initialized individual. The fitness is indicated by the intensity. (a) Dimension of 4×4. (b) Dimension of 8×8. (c) Dimension of 16×16. 4 GHz Six-Cores. Then the best results of these runs are used as initial populations for further 120 runs and so on, until the results did not further improve. For one run of the algorithm we used a population size of 1000 and computed between 1000 and 4000 generations dependent on the intended dimension of the optical network.

The result is a subset of the entire population that represents a diverse set of methods for achieving the desired goal. 2 Evolutionary Model The work in this paper employs a page-based linear genetic programming learning model [13]. A population of individual solutions are randomly initialized in both size (number of commands) and content (command sequences) in a linear sequence [4, 5, 15]. At each generational stage, a small subset of the current population is randomly selected (the tournament), search operators are then applied; the best resulting individuals of the tournament then replace the worst and are placed back in the population.

The well-known frequency reuse schemes 1x3x3, whereby the entire bandwidth is divided into 3 nonoverlapping groups and assigned to 3 co-site sectors within each cell, have been used in our model. Further work must be done on algorithmic approach. Keywords: LTE, Robustness, SINR, Interference, Frequency, Optimization. 1 Introduction The Long Term Evolution is a new air-interface designed by the Third Generation Partnership Project (3GPP) [6]. Its goal is to achieve additional substantial leaps in terms of service provisioning and cost reduction.

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