Swarm, Evolutionary, and Memetic Computing 5th International Conference, SEMCCO 2014, Bhubaneswar, India, December 18-20, 2014, Revised Selected Papers /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Panigrahi, Bijaya Ketan (Επιμελητής έκδοσης), Suganthan, Ponnuthurai Nagaratnam (Επιμελητής έκδοσης), Das, Swagatam (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2015.
• Σειρά: Lecture Notes in Computer Science, 8947
• Θέματα: Computer science.; Computer communication systems.; Software engineering.; Computers.; Algorithms.; Artificial intelligence.; Pattern recognition.; Computer Science.; Computation by Abstract Devices.; Artificial Intelligence (incl. Robotics).; Algorithm Analysis and Problem Complexity.; Pattern Recognition.; Computer Communication Networks.; Software Engineering.

This volume constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2014, held in Bhubaneswar, India, in December 2014. The total of 96 papers presented in this volume was carefully reviewed and selecte...

Πίνακας περιεχομένων:

• Differential Evolution with Two Subpopulations
• Intelligent Water Drops Algorithm for Multimodal Spaces
• Glowworm Swarm based Informative attribute
• Simultaneous Feature Selection and Classification
• TLBO Based Hybrid Forecasting Model for Prediction of Exchange Rates
• Evaluating Internet Information Search Channels using Hybrid MCDM technique
• Image Restoration with Fuzzy Coefficient Driven Anisotropic Diffusion
• Principal Component Analysis and General Regression Auto Associative Neural Network Hybrid as One-Class Classifier
• Software Effort Estimation Using Functional Link Neural Networks Tuned with Active Learning and Optimized with Particle Swarm Optimization
• Fraud Detection in Financial Statements using Evolutionary Computation based Rule Miners
• A Fuzzy Entropy based Multi-level Image Thresholding using Differential Evolution.