
- Instructor: Bettina Haas
- Lectures: 47
- Students: 11174
- Duration: 10 weeks
This course provides an extensive quick guide to Genetic Algorithms (GAs), detailing the principles of this search-based optimization technique inspired by natural selection and genetics. The guide systematically introduces foundational concepts, starting with optimization and the general structure of GAs, including basic terminology like population, chromosomes, and fitness function. Subsequent sections provide in-depth explanations of the core genetic operators, such as different methods for parent selection, various crossover operators (like one-point and uniform), and mutation operators (such as bit flip and swap mutation), all designed to introduce genetic variation. Finally, the tutorial discusses advanced implementation aspects, including survivor selection policies, termination conditions, models of lifetime adaptation (Lamarckian and Baldwinian), theoretical background like the Schema Theorem, and numerous real-world application areas where GAs are employed.
Genetic Algorithms Principles and Practice Course is based on textbook learning material by Academy Europe.

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Audience
This course by Academy Europe aims at imparting quality education and training to students.
Academy Europe is dedicated to its students, their specific learning requirements, and their overall learning success.
This course is directed toward a student-centered, independent study, asynchronous learning approach.
After completing this course on Academy Europe, students will get self improvement and promotion in their careers.
This course is based on at least two learning skills which are provided to the users through audio & visuals, videos, verbal presentations and articles, all of which are asynchronized with distance education approach.
Prerequisites
Before you start proceeding with this course on Academy Europe, we are assuming that you have a good aptitude and can think logically. You should want to try something different.
Ideal candidates for the course would typically possess:
– Discipline and attentiveness
– Ability to conduct research
– Ability to perform tasks with speed, efficiency, and accuracy
– Analytical judgment
– Patience to interpret technical/scientific data
– A willingness to learn, roll up your sleeves and work toward your dream!
– A computer, tablet or smartphone and an internet connection
– Basic computer skills
Curriculum
- 8 Sections
- 47 Lessons
- 10 Weeks
- 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
Requirements
- Discipline and attentiveness
- Ability to conduct research
- Ability to perform tasks with speed, efficiency, and accuracy
- Analytical judgment
- Patience to interpret technical/scientific data
- A willingness to learn, roll up your sleeves and work toward your dream!
- A computer, tablet or smartphone and an internet connection
- Basic computer skills
Features
- Before you start proceeding with this course on Academy Europe, we are assuming that you have a good aptitude and can think logically. You should want to try something different.
Target audiences
- This course by Academy Europe aims at imparting quality education and training to students.
- Academy Europe is dedicated to its students, their specific learning requirements, and their overall learning success.
- This course is directed toward a student-centered, independent study, asynchronous learning approach.
- After completing this course on Academy Europe, students will get self improvement and promotion in their careers.
- This course is based on at least two learning skills which are provided to the users through audio & visuals, videos, verbal presentations and articles, all of which are asynchronized with distance education approach.