Nature-Inspired Optimization Algorithms: Recent Advances in Natural Computing and Biomedical Applications

This book explores the use of data mining and intelligent computing methods to advance biomedical applications and nature-inspired computing for biomedical systems. It focuses on developing enhanced, hybrid, adaptive, or improved versions of basic algorithms to solve complex problems in various fields.

Format: Hardback
Length: 168 pages
Publication date: 08 February 2021
Publisher: De Gruyter

This comprehensive book delves into the intricate interplay of data mining and intelligent computing methodologies in advancing biomedical applications and algorithms inspired by nature. It aims to propose enhanced, hybrid, adaptive, or improved versions of fundamental algorithms, surpassing their performance and convergence metrics. Within this dynamic and rapidly evolving interdisciplinary domain, a wide array of theories and methodologies are being explored and developed to address complex and challenging problems.

The analysis and processing of data have emerged as pivotal areas of focus within the research community and information society. The rise of natural computing has led to the increasing popularity of meta-heuristic or bio-inspired algorithms due to their remarkable potential to tackle computationally intractable optimization dilemmas across various fields, including medical, engineering, military, space, and industry. The primary reason behind the success of nature-inspired algorithms lies in their ability to solve problems effectively. Nature-inspired optimization techniques offer adaptive computational tools for tackling complex optimization problems across diverse engineering applications.

The book will provide a comprehensive coverage of various topics, including:

Neural Computation: Exploring the workings of neural networks and their applications in biomedical systems.

Evolutionary Computing Methods: Examining evolutionary algorithms and their ability to solve complex optimization problems.

Neuroscience-driven AI Inspired Algorithms: Investigating algorithms inspired by neuroscience principles and their applications in medical imaging, diagnosis, and treatment.

Biological System-based Algorithms: Analyzing algorithms modeled after biological systems and their efficiency in solving optimization problems.

Hybrid and Intelligent Computing Algorithms: Discussing the integration of data mining and intelligent computing techniques to enhance the performance of biomedical systems.

Application of Natural Computing: Exploring the use of natural phenomena, such as patterns, structures, and behaviors, to develop innovative algorithms.

Review and State of Art Analysis of Optimization Algorithms: Providing a comprehensive review of existing optimization algorithms and their applications in biomedical systems.

Molecular and Quantum Computing Applications: Discussing the potential of molecular and quantum computing in advancing biomedical research and treatments.

Swarm Intelligence: Analyzing swarm intelligence algorithms and their applications in optimization, collective decision-making, and search problems.

Population-based Algorithm and Other Optimizations: Exploring other population-based algorithms and their effectiveness in solving optimization problems.

By exploring these topics in depth, this book aims to provide a comprehensive understanding of the latest advancements in biomedical applications and algorithms inspired by nature. It serves as a valuable resource for researchers, practitioners, and students interested in this field, facilitating the development of innovative solutions to complex biomedical challenges.

Weight: 426g
Dimension: 175 x 246 x 16 (mm)
ISBN-13: 9783110676068