This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.
Author(s): Nilanjan Dey
Series: Springer Tracts in Nature-Inspired Computing
Publisher: Springer
Year: 2023
Language: English
Pages: 253
City: Singapore
Preface
Contents
Editor and Contributors
1 Variants of Genetic Algorithms and Their Applications
1 Introduction
2 Background
3 Genetic Algorithms and Their Variants
3.1 Variants of GA Based on Operators and Chromosomes
3.2 Variants of GA Based on Crossover
3.3 Chaotic Genetic Algorithm (CGA)
3.4 Adaptive Genetic Algorithm
3.5 Niching Genetic Algorithm
3.6 Interactive Genetic Algorithm
3.7 Saw-Tooth Genetic Algorithm
3.8 Differential Evolution Genetic Algorithm
4 Applications
5 Future Research Directions
6 Conclusions
References
2 Genetic Algorithm Applications for Challenging Real-World Problems: Some Recent Advances and Future Trends
1 Introduction
1.1 Motivation
1.2 Main Contributions
1.3 Structure of This Chapter
2 Genetic Algorithms: Fundamentals and Background
2.1 Natural Evolution and Evolutionary Computing
2.2 Genetic Algorithms
3 Genetic Algorithms for Real-World Problems: Recent Advances
3.1 Reverse Engineering for Manufacturing
3.2 Medicine and Bioinformatics
3.3 Computer Animation and Video Games
3.4 Robotics
4 Genetic Algorithms for Real-World Problems: Future Trends and Current Challenges
5 Discussion
5.1 Limitations of Genetic Algorithms
5.2 Limitations of The Study in This Chapter
6 Conclusions
References
3 Genetic Algorithm for Route Optimization
1 Introduction
2 Related Works
3 Principle of Genetic Algorithm
3.1 Genetic Coding of the Problem
3.2 Creation of the Initial Population
3.3 Evaluation of Fitness Function
3.4 Application of Reproduction Process
3.5 Application of the Crossover Operation
3.6 Application of the Mutation Operation
3.7 Creating a New Generation and Finishing the Algorithm
4 Parameter Selection in Genetic Algorithm
4.1 Population Size
4.2 Crossover Probability
4.3 Mutation Probability
4.4 Generation Range
4.5 Selection Strategies
5 GA Applications in Route Optimization
5.1 Routing Optimization for Logistics
5.2 Power Line Route Optimization
5.3 Robot Route Optimization
5.4 Network Route Optimization
6 GA for Power Transmission Line Route Optimization: An Application
7 Discussions
8 Conclusion and Future Works
References
4 Design Weight Minimization of a Reinforced Concrete Beam Through Genetic Algorithm and Its Variants
1 Introduction
2 Related Previous Background Studies
3 Design of the Reinforced Concrete Beam
4 Genetic Algorithms
4.1 Alternation of Generations in Nature
4.2 General Structure of Standard Genetic Algorithms
4.3 Variants of Genetic Algorithms
5 Design Examples
6 Conclusions
References
5 IGA: An Improved Genetic Algorithm for Real-Optimization Problem
1 Introduction
2 Related Work
3 Improve Genetic Algorithm
3.1 Adaptive Crossover and Mutation
3.2 Selection Operator
4 Computational Evaluation
4.1 Unconstrained CEC 2018
4.2 Non-parametric and Convergence History Testing
5 Nonlinear Optimization Test
5.1 Problem Statement
5.2 Results and Discussion
5.3 Success Rate
6 Conclusions and Future Work
References
6 Application of Genetic Algorithm-Based Controllers in Wind Energy Systems for Smart Energy Management
1 Introduction
1.1 Research Gap Analysis
1.2 Novelty of the Chapter
1.3 Organization of the Chapter
1.4 Contribution of the Work
2 Investigated Power System
2.1 Wind Turbine Generator
2.2 Battery Energy Storage System
2.3 Fuel Cell
3 Secondary Controller Design
4 Genetic Algorithm
5 Performance Analysis
5.1 Case 1: Performance Investigation with/without BESS in Wind Power Plant
5.2 Case 2: Performance Investigation with/without FC in Wind Power Plant
5.3 Case 3: Performance Investigation with/without FC in Wind Power Plant
5.4 Performance Investigation with/without BESS & FC in Wind Power Plant
6 Conclusion
7 Challenges and Limitations
Appendix [37]
References
7 Application of Genetic Algorithm in Predicting Mental Illness: A Case Study of Schizophrenia
1 Introduction
2 Theoretical Framework
2.1 Machine Learning in Medicine
2.2 Schizophrenia
3 Methodology
4 Results and Discussion
5 Conclusion
References
8 Comparison of Biologically Inspired Algorithm with Socio-inspired Technique on Load Frequency Control of Multi-source Single-Area Power System
1 Introduction
2 Related Works
3 Employed Optimization Techniques
3.1 Genetic Algorithm
3.2 Differential Evolution Algorithm
3.3 Socio-inspired Algorithm
4 Result and Discussion
5 Conclusion
Appendix
References
9 Genetic Algorithm and Accelerating Fuzzification for Optimum Sizing and Topology Design of Real-Size Tall Building Systems
1 Introduction
2 Related Works on Real-Size Tall Building Systems
3 Mathematical Formation of Size and Topology Optimization of Real-Size Tall Building Systems
4 Genetic Algorithm (GA)
4.1 Standard GA
4.2 Fuzzy-Assisted GA with Bi-Linear Membership Function
5 Discussion on the Optimum Design Examples of Real-Size Tall Building Systems
5.1 Outrigger-Braced Tall Building with Inclined Truss Belt
5.2 Diagrid Structure
6 Conclusions
References
10 Evaluation of Underwater Images Using Genetic Algorithm-Monitored Preprocessing and Morphological Segmentation
1 Introduction
2 Literature Review
3 Methodology
3.1 Database
3.2 Image Enhancement
3.3 Segmentation
3.4 Performance Evaluation and Validation
4 Result and Discussions
5 Conclusion
References
