By Anna I. Esparcia-Alcázar, Antonio M. Mora
This e-book constitutes the completely refereed post-conference complaints of the overseas convention at the functions of Evolutionary Computation, EvoApplications 2014, held in Granada, Spain, in April 2014, colocated with the Evo* 2014 occasions EuroGP, EvoCOP, and EvoMUSART.
The seventy nine revised complete papers offered have been conscientiously reviewed and chosen from 128 submissions. EvoApplications 2014 consisted of the subsequent thirteen tracks: EvoCOMNET (nature-inspired innovations for telecommunication networks and different parallel and disbursed systems), EvoCOMPLEX (evolutionary algorithms and complicated systems), EvoENERGY (evolutionary computation in strength applications), EvoFIN (evolutionary and traditional computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in snapshot research, sign processing, and development recognition), EvoINDUSTRY (nature-inspired strategies in commercial settings), EvoNUM (bio-inspired algorithms for non-stop parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for chance administration, safeguard and defence applications), EvoROBOT (evolutionary computation in robotics), EvoSTOC (evolutionary algorithms in stochastic and dynamic environments), and EvoBio (EC and similar suggestions in bioinformatics and computational biology).
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Additional info for Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers
Build a GP tree. Each GP tree represents an individual. Basically, a group of individuals which are the candidate solutions to the problem are generated by GP in each generation. How well the individuals solve the problem is evaluated by using a ﬁtness function. 1 Feature Sets and Operators Selecting the right feature set is a diﬃcult problem and a key point to obtain successful results in GP and other machine learning techniques . In our model, the information collected from past interactions and recommendations of neighbors form the feature set.
219(3), 738–750 (2012) 4. : New Results about Multi-band Uncertainty in Robust Optimization. In: Klasing, R. ) SEA 2012. LNCS, vol. 7276, pp. 63–74. Springer, Heidelberg (2012) 5. : A new theoretical framework for robust optimization under multi-band uncertainty. , et al. ) Operations Research Proceedings 2012, pp. 115–121. Springer, Heidelberg (2014) 6. : 0–1 multiband robust optimization. , et al. ) Operations Research Proceedings 2013, pp. 89–95. Springer, Heidelberg (to appear, 2014) 7. : The price of robustness.
Evolutionary computation techniques for intrusion detection in mobile ad hoc networks. Computer Networks 55(15), 3441–3457 (2011) 23. : A Multi-objective Optimisation Approach to IDS Sensor Placement. , Corchado, E. ) CISIS 2009. ASC, vol. 63, pp. 101– 108. Springer, Heidelberg (2009) 24. : Peer proﬁle based trust model for p2p systems using genetic algorithm. Peer-to-Peer Networking and Applications 5(1), 92–103 (2012) 25. : Genetic Programming: On the Programming of Computers by Means of Natural Selection.
Applications of Evolutionary Computation: 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers by Anna I. Esparcia-Alcázar, Antonio M. Mora