Genetic Algorithms Principles and Practice
Curriculum
- 8 Sections
- 47 Lessons
- 10 Weeks
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- Introduction1
- A Beginner's Walkthrough of the Genetic Algorithm Lifecycle4
- Briefing Document: Genetic Algorithms5
- Foundations of Evolutionary Computation: A Comprehensive Study of Genetic Algorithms13
- 4.11.0 Introduction to Genetic Algorithms and the Optimization Landscape
- 4.22.0 The Motivation for Heuristic Search: Why We Need Genetic Algorithms
- 4.33.0 Foundational Terminology and GA Architecture
- 4.44.0 Genotype Representation: Encoding Solutions for Evolution
- 4.55.0 The Population: Managing the Pool of Candidate Solutions
- 4.66.0 The Fitness Function: Quantifying Solution Quality
- 4.77.0 Parent Selection: Driving the Search Towards Fitter Solutions
- 4.88.0 Crossover and Mutation: Generating Novel Solutions
- 4.99.0 Survivor Selection and Elitism: Shaping the Next Generation
- 4.1010.0 Termination Criteria
- 4.1111.0 Advanced Concepts in Genetic Algorithms
- 4.1212.0 Effective Implementation and Diverse Applications
- 4.1313.0 Recommended Further Readings
- Strategic Brief: Leveraging Genetic Algorithms for Complex Optimization6
- 5.11. The Strategic Imperative: Solving Intractable Business Problems
- 5.22. Core Principles: A Nature-Inspired Approach to Problem-Solving
- 5.33. A Balanced Assessment: Advantages and Strategic Limitations
- 5.44. Key Implementation Considerations for Success
- 5.55. High-Value Application Domains
- 5.66. Concluding Strategic Recommendation
- A Practical Implementation Guide to Genetic Algorithms9
- 6.11.0 Introduction to Genetic Algorithms for Optimization
- 6.22.0 The Core Architecture and Terminology
- 6.33.0 Step 1: Designing the Genotype – Representing Solutions
- 6.44.0 Step 2: Population Management – Initialization and Evolution
- 6.55.0 Step 3: Evaluating Solutions – The Fitness Function
- 6.66.0 Step 4: The Engine of Evolution – Core Genetic Operators
- 6.77.0 Step 5: Managing Generations – Survivor Selection and Termination
- 6.88.0 Advanced Implementation Strategies and Best Practices
- 6.99.0 Application Areas and Further Learning
- A Gentle Introduction to Genetic Algorithms: Solving Problems with Evolution5
- Study Guide for Genetic Algorithms4
Essay Questions
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