Artificial Intelligence, Evolutionary Computing and Metaheuristics

Artificial Intelligence, Evolutionary Computing and Metaheuristics
Author :
Publisher : Springer
Total Pages : 797
Release :
ISBN-10 : 9783642296949
ISBN-13 : 3642296947
Rating : 4/5 (947 Downloads)

Book Synopsis Artificial Intelligence, Evolutionary Computing and Metaheuristics by : Xin-She Yang

Download or read book Artificial Intelligence, Evolutionary Computing and Metaheuristics written by Xin-She Yang and published by Springer. This book was released on 2012-07-27 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo search, and multiobjective optimization and many applications. These reviews and chapters not only provide a timely snapshot of the state-of-art developments, but also provide inspiration for young researchers to carry out potentially ground-breaking research in the active, diverse research areas in artificial intelligence, cryptography, machine learning, evolutionary computation, and nature-inspired metaheuristics. This edited book can serve as a timely reference for graduates, researchers and engineers in artificial intelligence, computer sciences, computational intelligence, soft computing, optimization, and applied sciences.


Artificial Intelligence, Evolutionary Computing and Metaheuristics Related Books

Artificial Intelligence, Evolutionary Computing and Metaheuristics
Language: en
Pages: 797
Authors: Xin-She Yang
Categories: Technology & Engineering
Type: BOOK - Published: 2012-07-27 - Publisher: Springer

GET EBOOK

Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it
Modeling, Analysis, and Applications in Metaheuristic Computing
Language: en
Pages: 0
Authors: Peng-Yeng Yin
Categories: Computer simulation
Type: BOOK - Published: 2012 - Publisher:

GET EBOOK

"This book is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing, providin
Handbook of AI-based Metaheuristics
Language: en
Pages: 419
Authors: Anand J. Kulkarni
Categories: Computers
Type: BOOK - Published: 2021-09-01 - Publisher: CRC Press

GET EBOOK

At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growi
Metaheuristics in Machine Learning: Theory and Applications
Language: en
Pages: 765
Authors: Diego Oliva
Categories: Computational intelligence
Type: BOOK - Published: - Publisher: Springer Nature

GET EBOOK

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolut
Metaheuristics for Machine Learning
Language: en
Pages: 231
Authors: Mansour Eddaly
Categories: Computers
Type: BOOK - Published: 2023-03-13 - Publisher: Springer Nature

GET EBOOK

Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression