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Applied Evolutionary Algorithms for Engineers using Python (Hardcover)
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Applied Evolutionary Algorithms for Engineers using Python (Hardcover)
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This book meant for those who seek to apply evolutionary algorithms
to problems in engineering and science. To this end, it provides
the theoretical background necessary to the understanding of the
presented evolutionary algorithms and their shortcomings, while
also discussing themes that are pivotal to the successful
application of evolutionary algorithms to real-world problems. The
theoretical descriptions are illustrated with didactical Python
implementations of the algorithms, which not only allow readers to
consolidate their understanding, but also provide a sound starting
point for those intending to apply evolutionary algorithms to
optimization problems in their working fields. Python has been
chosen due to its widespread adoption in the Artificial
Intelligence community. Those familiar with high level languages
such as MATLAB (TM) will not have any difficulty in reading the
Python implementations of the evolutionary algorithms provided in
the book. Instead of attempting to encompass most of the existing
evolutionary algorithms, past and present, the book focuses on
those algorithms that researchers have recently applied to
difficult optimization problems, such as control problems with
continuous action spaces and the training of high-dimensional
convolutional neural-networks. The basic characteristics of
real-world optimization problems are presented, together with
recommendations on its proper application to evolutionary
algorithms. The applied nature of the book is reinforced by the
presentation of successful cases of the application of evolutionary
algorithms to optimization problems. This is complemented by Python
source codes, giving users an insight into the idiosyncrasies of
the practical application of evolutionary algorithms.
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