Swarm, Evolutionary, and Memetic Computing

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

AvBijaya Ketan Panigrahi,Ponnuthurai Nagaratnam Suganthan

Häftad, Engelska, 2015

Del 8947 i serien Lecture Notes in Computer Science

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Beskrivning

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

Utgivningsdatum:2015-08-11
Mått:155 x 235 x 48 mm
Vikt:1 334 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:881
Förlag:Springer International Publishing AG
ISBN:9783319202938

Utforska kategorier

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

Innehållsförteckning

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.