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020 _a9789811536854
_9978-981-15-3685-4
024 7 _a10.1007/978-981-15-3685-4
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
245 1 0 _aDeep Neural Evolution
_h[electronic resource] :
_bDeep Learning with Evolutionary Computation /
_cedited by Hitoshi Iba, Nasimul Noman.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aXII, 438 p. 221 illus., 107 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x2627-6461
505 0 _aChapter 1: Evolutionary Computation and meta-heuristics -- Chapter 2: A Shallow Introduction to Deep Neural Networks -- Chapter 3: On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks -- Chapter 4: Automated development of DNN based spoken language systems using evolutionary algorithms -- Chapter 5: Search heuristics for the optimization of DBN for Time Series Forecasting -- Chapter 6: Particle Swarm Optimisation for Evolving Deep Convolutional Neural Networks for Image Classification: Single- and Multi-objective Approaches -- Chapter 7: Designing Convolutional Neural Network Architectures Using Cartesian Genetic Programming -- Chapter 8: Fast Evolution of CNN Architecture for Image Classificaiton -- Chapter 9: Discovering Gated Recurrent Neural Network Architectures -- Chapter 10: Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution -- Chapter 11: Neuroevolution of Generative Adversarial Networks -- Chapter 12: Evolving deep neural networks for X-ray based detection of dangerous objects -- Chapter 13: Evolving the architecture and hyperparameters of DNNs for malware detection -- Chapter 14: Data Dieting in GAN Training -- Chapter 15: One-Pixel Attack: Understanding and Improving Deep Neural Networks with Evolutionary Computation.
520 _aThis book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL. EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research —from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN). This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
650 0 _aMachine learning.
650 0 _aNeural networks (Computer science) .
650 1 4 _aMachine Learning.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
700 1 _aIba, Hitoshi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aNoman, Nasimul.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811536847
776 0 8 _iPrinted edition:
_z9789811536861
776 0 8 _iPrinted edition:
_z9789811536878
830 0 _aNatural Computing Series,
_x2627-6461
856 4 0 _uhttps://doi.org/10.1007/978-981-15-3685-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176117
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