The book's unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.
Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader's understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.
Among the subjects of the 12 chapters are:
- A novel method based on TRIZ to map real-world problems to nature problems
- Applications of cuckoo search algorithm for optimization problems
- Performance analysis of nature-inspired algorithms in breast cancer diagnosis
- Nature-inspired computation in data mining
- Hybrid bat-genetic algorithm–based novel optimal wavelet filter for compression of image data
- Efficiency of finding best solutions through ant colony optimization techniques
- Applications of hybridized algorithms and novel algorithms in the field of machine learning.
Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.