000			01786 a2200169 4500
005			20241223103403.0
020			_a9781835887744
082			_a006.31 _bWIR-H
100			_aEyal, Wirsansky,
245		0	_aHands-on Genetic Algorithms with Python
260			_aBirmingham _bPackt, _c2020
300			_avii 319 p _c23.5 x 19.1 x 1.84 cm
500			_aGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.
504			_aIncluding with index
650			_aComputer Science, Python, Algorithm, Genetic Algorithm, Deep Learning,
942			_cBK
999			_c33263 _d33263