£82.41

Springer Biologically-Inspired Optimisation Methods - Book 210

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Description

Explore the connection between natural processes and computational problem-solving with Biologically-Inspired Optimisation Methods. This volume from the Studies in Computational Intelligence series (Volume 210) examines how biological systems adapt and survive in changing environments to inspire modern technology. Throughout history, nature has developed efficient ways to thrive amidst environmental upheavals. This book applies those lessons to the realm of optimisation problems. It covers a wide range of meta-heuristic search algorithms that draw direct inspiration from biological systems. Readers will find information on key methods such as genetic algorithms, particle swarm optimisation, and ant colony optimisation. Designed for those interested in parallel algorithms, systems, and applications, this text provides a foundation for understanding how natural processes can be emulated in the artificial world. It is a valuable resource for students and professionals working within the fields of artificial intelligence and machine learning.

Key Features

Covers essential meta-heuristic search algorithms including genetic algorithms and particle swarm optimisation.

Examines how biological adaptation and survival mechanisms can be applied to human-made artificial systems.

Part of the Studies in Computational Intelligence series, specifically volume 210.

Focuses on the application of parallel algorithms and systems to complex optimisation problems.

Provides insight into ant colony optimisation and other nature-inspired computational methods.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
25 May 2009
Listed Since
23 March 2009

Barcode

No barcode data available

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