Chapter 1: Introduction to Genetic Algorithms
- Lesson 1: What is a Genetic Algorithm?
- Lesson 2: Historical Background and Key Concepts
- Lesson 3: Components of a GA (Population, Chromosomes, Genes, etc.)
- Lesson 4: Applications of Genetic Algorithms
- Lesson 5: Overview of the Course
Chapter 2: Foundations of Evolutionary Computation
- Lesson 1: Biological Inspiration: Darwinian Evolution
- Lesson 2: Genetic Operators: Selection, Crossover, Mutation
- Lesson 3: Fitness Function and Survival of the Fittest
- Lesson 4: Exploration vs Exploitation
- Lesson 5: Challenges and Limitations
Chapter 3: Basic Variants of Genetic Algorithms
- Lesson 1: Simple Genetic Algorithm (SGA)
- Lesson 2: Steady-State Genetic Algorithm
- Lesson 3: Generational Genetic Algorithm
- Lesson 4: Elitist Genetic Algorithm
- Lesson 5: Adaptive Genetic Algorithm
Chapter 4: Selection Methods in Genetic Algorithms
- Lesson 1: Roulette Wheel Selection
- Lesson 2: Tournament Selection
- Lesson 3: Rank-Based Selection
- Lesson 4: Stochastic Universal Sampling (SUS)
- Lesson 5: Truncation Selection
Chapter 5: Crossover Operators
- Lesson 1: Single-Point Crossover
- Lesson 2: Two-Point Crossover
- Lesson 3: Uniform Crossover
- Lesson 4: Arithmetic Crossover
- Lesson 5: Simulated Binary Crossover (SBX)
Chapter 6: Mutation Operators
- Lesson 1: Bit Flip Mutation
- Lesson 2: Gaussian Mutation
- Lesson 3: Polynomial Mutation
- Lesson 4: Boundary Mutation
- Lesson 5: Non-Uniform Mutation
Chapter 7: Encoding Techniques in GAs
- Lesson 1: Binary Encoding
- Lesson 2: Real-Valued Encoding
- Lesson 3: Permutation Encoding
- Lesson 4: Tree-Based Encoding (for Genetic Programming)
- Lesson 5: Gray Code Encoding
Chapter 8: Fitness Evaluation Techniques
- Lesson 1: Penalty-Based Methods (for Constraint Handling)
- Lesson 2: Feasibility-Preserving Crossover and Mutation
- Lesson 3: Fitness Sharing
- Lesson 4: Crowding Distance Sorting
- Lesson 5: Inverted Fitness Ranking
Chapter 9: Advanced Techniques in GAs
- Lesson 1: Co-Evolutionary Genetic Algorithms
- Lesson 2: Lamarckian Genetic Algorithm
- Lesson 3: Baldwinian Genetic Algorithm
- Lesson 4: Self-Adaptive Genetic Algorithm
- Lesson 5: Island Model Genetic Algorithm
Chapter 10: Genetic Algorithms in Scheduling
- Lesson 1: Basics of Job-Shop Scheduling
- Lesson 2: Representing Scheduling Problems in GAs
- Lesson 3: Fitness Functions for Scheduling
- Lesson 4: Challenges in Scheduling Optimization
- Lesson 5: Case Study: Solving Scheduling Problems
Chapter 11: Genetic Algorithms for TSP
- Lesson 1: Introduction to the Traveling Salesman Problem
- Lesson 2: Permutation Encoding for TSP
- Lesson 3: Crossover and Mutation in TSP
- Lesson 4: Solving TSP with GAs
- Lesson 5: Performance Evaluation
Chapter 12: Feature Selection in Machine Learning
- Lesson 1: Importance of Feature Selection
- Lesson 2: Encoding Features for GAs
- Lesson 3: Fitness Evaluation for Feature Selection
- Lesson 4: Applications in Machine Learning
- Lesson 5: Case Studies
Chapter 13: Basic Applications of Genetic Algorithms
- Lesson 1: Industrial Applications
- Lesson 2: Healthcare Applications
- Lesson 3: Engineering Design Optimization
- Lesson 4: Financial Market Predictions
- Lesson 5: Education and Research
Chapter 14: Implementing Genetic Algorithms
- Lesson 1: Pseudocode for a Basic GA
- Lesson 2: Python Implementation of a GA
- Lesson 3: Performance Metrics for GAs
- Lesson 4: Tuning GA Parameters
- Lesson 5: Debugging Common Issues
Chapter 15: Challenges in Genetic Algorithms
- Lesson 1: Premature Convergence
- Lesson 2: Balancing Exploration and Exploitation
- Lesson 3: Fitness Landscape Analysis
- Lesson 4: Scalability Issues
- Lesson 5: Addressing Noisy Fitness Functions
Chapter 16: Software Libraries for GAs
- Lesson 1: Introduction to DEAP (Distributed Evolutionary Algorithms in Python)
- Lesson 2: PyGAD for Genetic Algorithms
- Lesson 3: Evolutionary Algorithms in MATLAB
- Lesson 4: Java Libraries for GAs
- Lesson 5: R and Other Tools
Chapter 17: Final Project
- Lesson 1: Overview of the Final Project
- Lesson 2: Problem Selection
- Lesson 3: Developing the Solution
- Lesson 4: Performance Evaluation
- Lesson 5: Project Presentation
Chapter 1: Multi-Objective Optimization
- Lesson 1: Introduction to Multi-Objective Problems and Pareto Front
- Lesson 2: Overview of NSGA-II Algorithm: Theory and Implementation
- Lesson 3: NSGA-III: Advancements over NSGA-II
- Lesson 4: Strength Pareto Evolutionary Algorithm (SPEA2)
- Lesson 5: Pareto Archived Evolution Strategy (PAES)
- Lesson 6: Basics of MOEA/D
- Lesson 7: Applications of Multi-Objective Optimization in Real-World Problems
- Lesson 8: Performance Metrics for Multi-Objective Algorithms
Chapter 2: Hybrid Genetic Algorithms
- Lesson 1: Concept of Hybridization in Genetic Algorithms
- Lesson 2: Combining Genetic Algorithms with Simulated Annealing
- Lesson 3: GA + Particle Swarm Optimization (GA-PSO): Case Studies
- Lesson 4: Genetic Algorithm + Tabu Search for Complex Optimization
- Lesson 5: Genetic Algorithm + Differential Evolution
- Lesson 6: Genetic Algorithm + Ant Colony Optimization
- Lesson 7: Applications and Benefits of Hybrid Approaches
Chapter 3: Memetic Algorithms
- Lesson 1: Memetic Algorithms: An Overview and Basic Concepts
- Lesson 2: Incorporating Local Search Heuristics in GAs
- Lesson 3: Designing Memetic Algorithms for Specific Problem Domains
- Lesson 4: Computational Complexity and Efficiency of Memetic Algorithms
- Lesson 5: Applications in Bioinformatics, Engineering, and Scheduling
Chapter 4: Parallel and Distributed Genetic Algorithms
- Lesson 1: Need for Parallelism in Genetic Algorithms
- Lesson 2: Island Model Genetic Algorithms
- Lesson 3: Master-Slave Parallel Genetic Algorithms
- Lesson 4: Implementing Parallelism with GPU and Cloud Computing
- Lesson 5: Case Studies and Applications in High-Dimensional Problems
Chapter 5: Co-Evolutionary Algorithms
- Lesson 1: Introduction to Co-Evolutionary Algorithms
- Lesson 2: Competitive Co-Evolution vs Cooperative Co-Evolution
- Lesson 3: Designing Interdependent Populations in GAs
- Lesson 4: Applications in Artificial Life and Robotics
- Lesson 5: Performance Challenges in Co-Evolutionary Algorithms
Chapter 6: Dynamic Optimization with GAs
- Lesson 1: Challenges of Dynamic Optimization Problems
- Lesson 2: Adapting Genetic Algorithms for Changing Environments
- Lesson 3: Memory-Based and Diversity-Based Approaches
- Lesson 4: Case Studies in Real-Time Optimization Problems
- Lesson 5: Benchmarks and Metrics for Dynamic GAs
Chapter 7: Genetic Programming (GP)
- Lesson 1: Introduction to Genetic Programming
- Lesson 2: Tree-Based Representations and Operators in GP
- Lesson 3: Evolving Symbolic Expressions and Programs
- Lesson 4: Applications of GP in Automated Programming
- Lesson 5: Advanced Topics in GP: Cartesian GP and Linear GP
Chapter 8: Neuro-Genetic Algorithms
- Lesson 1: Combining GAs and Neural Networks for Optimization
- Lesson 2: Genetic Algorithms for Neural Architecture Search
- Lesson 3: Optimizing Hyperparameters of Neural Networks with GAs
- Lesson 4: Applications in Reinforcement Learning and Deep Learning
- Lesson 5: Advantages and Limitations of Neuro-Genetic Approaches
Chapter 9: Neuroevolution of Augmenting Topologies (NEAT)
- Lesson 1: Introduction to NEAT: Key Concepts and Algorithms
- Lesson 2: Evolution of Neural Network Topologies and Weights
- Lesson 3: Implementing NEAT for Complex Problems
- Lesson 4: Applications in Robotics and Autonomous Systems
- Lesson 5: Variants and Extensions of NEAT
Chapter 10: Surrogate-Assisted Genetic Algorithms
- Lesson 1: Need for Surrogate Models in Computationally Expensive Problems
- Lesson 2: Types of Surrogate Models: Kriging, RBF, and Others
- Lesson 3: Integrating Surrogate Models with Genetic Algorithms
- Lesson 4: Applications in Engineering and Scientific Research
- Lesson 5: Challenges and Future Directions in Surrogate-Assisted GAs
Chapter 11: Hyperparameter Optimization for Machine Learning Models
- Lesson 1: Overview of Hyperparameter Optimization
- Lesson 2: Using Genetic Algorithms for Tuning ML Models
- Lesson 3: Comparing GAs with Other Optimization Methods (Grid/Random Search, Bayesian)
- Lesson 4: Case Studies: Tuning Deep Learning Models with GAs
- Lesson 5: Challenges and Strategies for Large-Scale Tuning
Chapter 12: Quantum-Inspired Genetic Algorithms
- Lesson 1: Principles of Quantum Computing Applied to GAs
- Lesson 2: Quantum Chromosomes and Operators
- Lesson 3: Advantages of Quantum-Inspired GAs in Optimization
- Lesson 4: Case Studies in Cryptography and High-Dimensional Problems
- Lesson 5: Research Frontiers in Quantum-Inspired GAs
Chapter 13: Genetic Algorithms for Large-Scale Optimization
- Lesson 1: Challenges in Large-Scale Optimization
- Lesson 2: Specialized Crossover and Mutation Strategies
- Lesson 3: Dimensionality Reduction Techniques for GAs
- Lesson 4: Applications in Big Data and Engineering Optimization
- Lesson 5: Metrics for Evaluating Large-Scale GA Performance
Chapter 14: Self-Adaptive Genetic Algorithms
- Lesson 1: Concept of Self-Adaptation in Evolutionary Algorithms
- Lesson 2: Automatically Adjusting Mutation and Crossover Rates
- Lesson 3: Implementing Self-Adaptive Mechanisms in GAs
- Lesson 4: Case Studies in Real-World Optimization Problems
- Lesson 5: Challenges and Limitations of Self-Adaptive GAs
Chapter 15: Genetic Algorithms for Constraint Optimization
- Lesson 1: Introduction to Constraint Optimization
- Lesson 2: Types of Constraints in Optimization Problems (Hard and Soft Constraints)
- Lesson 3: Penalty-Based Approaches for Handling Constraints
- Lesson 4: Repair Mechanisms for Chromosomes
- Lesson 5: Feasibility-Preserving Crossover and Mutation Techniques
- Lesson 6: Case Studies in Scheduling and Design Optimization
- Lesson 7: Comparing GAs with Other Constraint Optimization Methods
Chapter 16: Multi-Modal Genetic Algorithms
- Lesson 1: Introduction to Multi-Modality in Optimization
- Lesson 2: Fitness Sharing and Crowding Methods
- Lesson 3: Locating Multiple Optima Using Niching Techniques
- Lesson 4: Applications in Multi-Modal Engineering Problems
- Lesson 5: Benchmarks and Performance Metrics
Chapter 17: Interactive Genetic Algorithms
- Lesson 1: Leveraging Human Input in GAs
- Lesson 2: Applications in Design and Aesthetic Optimization
- Lesson 3: Implementing User Feedback in Fitness Evaluation
- Lesson 4: Challenges of Interactive GAs
- Lesson 5: Future Trends in Interactive Optimization
Chapter 18: Bio-Inspired Variants
- Lesson 1: Mimicking Epigenetics in GAs
- Lesson 2: Horizontal Gene Transfer in Evolutionary Algorithms
- Lesson 3: Case Studies of Bio-Inspired Genetic Variants
- Lesson 4: Applications in Bioinformatics and Computational Biology
- Lesson 5: Future Directions in Bio-Inspired Optimization
Chapter 19: Genetic Algorithms in Generative Design
- Lesson 1: Introduction to Generative Design Concepts
- Lesson 2: Using GAs to Optimize Structural Designs
- Lesson 3: Applications in Architecture and Engineering
- Lesson 4: Tools and Software for Generative Design with GAs
- Lesson 5: Case Studies in Aerospace and Automotive Design
Chapter 20: Automated Theorem Proving with GAs
- Lesson 1: Introduction to Theorem Proving and Optimization
- Lesson 2: Using GAs to Evolve Mathematical Proofs
- Lesson 3: Applications in Algebra and Geometry Theorem Proving
- Lesson 4: Challenges and Limitations of GAs in Automated Reasoning
- Lesson 5: Research Opportunities in Automated Theorem Proving
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