Del 8947 - Lecture Notes in Computer Science

Swarm, Evolutionary, and Memetic Computing

5th International Conference, SEMCCO 2014, Bhubaneswar, India, December 18-20, 2014, Revised Selected Papers

Häftad, Engelska, 2015

AvBijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suganthan,Swagatam Das

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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 selected from 250 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.

Produktinformation

Utgivningsdatum2015-08-11
Mått155 x 235 x 48 mm
Vikt1 334 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor881
FörlagSpringer International Publishing AG
ISBN9783319202938

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Programmeringsböcker inom Data och IT
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Människa – datorinteraktion inom Data och IT

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.