Seven Metaheuristics to Learn for your Next Data Science Project
The video book Seven Metaheuristics to Learn for your Next Data Science Project will assist you in easily and clearly understanding the seven most popular nature-based or metaheuristic algorithms.
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The content of the book is as given below :
1. INTRODUCTION
1.1. Types of Metaheuristics
1.2. Applications in Data Science
1.3. Advantages and Limitations
1.4. Comparison with other optimization techniques
2. OVERVIEW OF METAHEURISTICS
2.1. Application of Metaheuristics
2.2. Application of Metaheuristics in Applied Fields
2.3. Classification of Metaheuristic Algorithms
2.4. Working Principle
2,5. Limitations of Metaheuristic Algorithms
2.5. Future Scopes of Metaheuristics
3. METHOD I: ARTIFICIAL NEURAL NETWORK OR ANN
4. METHOD II: POLYNOMIAL NEURAL NETWORK OR PNN
5. METHOD III: GLOW WORM ALGORITHM OR GWA
6. METHOD IV: MINE BLAST ALGORITHM OR MBA
7. METHOD V: WATER CYCLE ALGORITHM OR WCA
8. METHOD VI: DOLPHIN ECHOLOCATION ALGORITHM OR DEA
9. METHOD VII : GENETIC ALGORITHM OR GA
10. CONCLUSION
10.1. Project Ideas
10.2. Numerical Problems
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