1. Statistical Methods and Analyses for Optimization Algorithms and Artificial Intelligence
An Intuitive Guide and Practical Steps of Learning from Data
Author: Abdul Hanif Halim & Swagatam Das
Publisher: Partridge
Publication: 2026
ISBN: 9781543761399
About the Book
This book presents a practical and intuitive approach to statistical methods for analyzing optimization algorithms and artificial intelligence. It covers descriptive and inferential statistics, parametric and nonparametric methods, statistical comparisons, trend analysis, and Bayesian approaches. The book emphasizes practical application, helping readers understand how statistical techniques can be used to evaluate, compare, and interpret results from optimization algorithms and AI-related studies.
Book Overview
Statistical Analysis Framework
Table of Contents
Chapter 1 — Descriptive Statistics
Chapter 2 — Inferential Statistics
Chapter 3 — Parametric Tests
Chapter 4 — Nonparametric Tests
Chapter 5 — Nonparametric Ordered Alternatives
Chapter 6 — Post-Hoc Tests
Chapter 7 — Bayesian Tests
Chapter 8 — Book Summary, Remarks, and Future Prospects
Chapter 9 — Appendixes
2. Into a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control – Volume 1
Author: Abdul Hanif Abdul Halim, Swagatam Das & Idris Ismail
Publisher: Springer
Publication: 2024
ISBN: 978-3-031-74012-1
About the Book
This volume provides a comprehensive exploration of fundamental and advanced concepts in evolutionary and metaheuristic computing, with particular emphasis on the critical balance between exploration and exploitation. It examines how algorithms initialize their search, how exploration and exploitation mechanisms influence the search process, and how distributions and mathematical functions can be used to improve optimization performance.
Combining theoretical foundations with practical perspectives, the book provides researchers, postgraduate students, and practitioners with a deeper understanding of the mechanisms that influence the behaviour and effectiveness of evolutionary optimization algorithms.
3. Into a Deeper Understanding of Evolutionary Computing: Exploration, Exploitation, and Parameter Control – Volume 2
Author: Abdul Hanif Abdul Halim, Swagatam Das & Idris Ismail
Publisher: Springer
Publication: 2025
ISBN: 978-3-031-75576-7
About the Book
This volume extends the study of evolutionary and metaheuristic computing by focusing on the design, tuning, and control of algorithmic search mechanisms. It examines the roles of location, population size, and computational capacity, followed by systematic approaches to algorithm parameter tuning and parameter control.
The book brings together theoretical concepts and practical strategies for improving the adaptability, efficiency, and search performance of optimization algorithms. It is intended for researchers, postgraduate students, algorithm developers, and practitioners seeking a deeper understanding of parameter control and the mechanisms that shape evolutionary optimization.