04952nam 22006015 450 991076754650332120200704220155.0981-13-9263-310.1007/978-981-13-9263-4(CKB)4100000009158813(MiAaPQ)EBC5849813(DE-He213)978-981-13-9263-4(PPN)243768141(EXLCZ)99410000000915881320190810d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplied Nature-Inspired Computing: Algorithms and Case Studies /edited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (281 pages)Springer Tracts in Nature-Inspired Computing,2524-552X981-13-9262-5 Chapter 1. Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation -- Chapter 2. Detection of Breast Cancer using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly algorithm and Optimum Path Forest Classification -- Chapter 3. Recommending Healthy Personalized Daily Menus – A Cuckoo Search based Hyper-Heuristic Approach -- Chapter 4. A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network -- Chapter 5. An Application of Binary Grey Wolf Optimizer (BGWO) variants for Unit Commitment Problem -- Chapter 6. Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization -- Chapter 7. Chaotic Variants of Grasshopper Optimisation Algorithm and their application to Protein Structure Prediction -- Chapter 8. Examination of Retinal Anatomical Structures – A Study with Spider Monkey Optimization Algorithm -- Chapter 9. Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparative Study -- Chapter 10. Parallel-series System Optimization by Weighting Sum Methods and Nature-inspired Computing -- Chapter 11. Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends.This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.Springer Tracts in Nature-Inspired Computing,2524-552XComputational intelligenceAlgorithmsComputer science—MathematicsComputer simulationComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Mathematics of Computinghttps://scigraph.springernature.com/ontologies/product-market-codes/I17001Simulation and Modelinghttps://scigraph.springernature.com/ontologies/product-market-codes/I19000Computational intelligence.Algorithms.Computer science—Mathematics.Computer simulation.Computational Intelligence.Algorithm Analysis and Problem Complexity.Mathematics of Computing.Simulation and Modeling.006.38Dey Nilanjanedthttp://id.loc.gov/vocabulary/relators/edtAshour Amira Sedthttp://id.loc.gov/vocabulary/relators/edtBhattacharyya Siddharthaedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910767546503321Applied Nature-Inspired Computing: Algorithms and Case Studies3655397UNINA