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This 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.
This 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.
This book delivers theoretical and practical knowledge of Genetic
Algorithms (GA) for the purpose of practical applications. It
provides a methodology for a GA-based search strategy with the
integration of several Artificial Life and Artificial Intelligence
techniques, such as memetic concepts, swarm intelligence, and
foraging strategies. The development of such tools contributes to
better optimizing methodologies when addressing tasks from areas
such as robotics, financial forecasting, and data mining in
bioinformatics.The emphasis of this book is on applicability to the
real world. Tasks from application areas - optimization of the
trading rule in foreign exchange (FX) and stock prices, economic
load dispatch in power system, exit/door placement for evacuation
planning, and gene regulatory network inference in bioinformatics -
are studied, and the resultant empirical investigations demonstrate
how successful the proposed approaches are when solving real-world
tasks of great importance.
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