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Springer Benchmarks and Hybrid Algorithms in Optimization

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Description

Expand your technical knowledge with Benchmarks and Hybrid Algorithms in Optimization and Applications. This volume is part of the Springer Tracts in Nature-Inspired Computing series, offering a focused look at optimization techniques and their practical uses. Designed for professionals and students within the fields of computer science and artificial intelligence, this book covers essential concepts in machine learning and computing. It serves as a reliable resource for anyone studying how hybrid algorithms function and how benchmarks are applied to measure performance in complex optimization tasks. Whether you are researching new computational methods or looking for established frameworks in nature-inspired computing, this Springer publication provides the necessary academic foundation. It is an ideal addition to your technical library for staying informed about current developments in AI and machine learning applications.

Key Features

Part of the professional Springer Tracts in Nature-Inspired Computing series for academic reliability.

Covers essential topics in optimization and hybrid algorithm development.

Provides information on benchmarks used to evaluate computational performance.

Relevant for researchers and students in AI and machine learning disciplines.

Focuses on the practical applications of optimization in computer science.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 August 2024
Listed Since
25 August 2024

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