Genetic Algorithms in Elixir

Overview

Explore genetic algorithms in Elixir with Sean Moriarity and Bruce Tate. Learn problem-solving techniques, from basics to real-world applications, using this powerful tool in a familiar language.

Syllabus

• Introduction to Genetic Algorithms
Overview of Genetic Algorithms
Key Concepts: Chromosomes, Genes, Fitness Function
Natural Evolution vs. Genetic Algorithms
• Getting Started with Elixir
Introduction to Elixir
Setting Up the Development Environment
Basic Syntax and Functional Programming Concepts
• Implementing Genetic Algorithms in Elixir
Representing Solutions in Elixir
Designing the Fitness Function
Selection Mechanisms
Crossover Operators
Mutation Techniques
Elixir-specific Libraries and Tools for Genetic Algorithms
• Developing Problem-Solving Techniques
Solving Optimization Problems
Case Study: Traveling Salesman Problem
Adapting Genetic Algorithms to Different Problem Domains
• Real-World Applications
Genetic Algorithms in Machine Learning
Evolving Neural Networks
Industrial and Business Applications
• Advanced Topics in Genetic Algorithms
Hybrid Genetic Algorithms
Parallel and Distributed Genetic Algorithms with Elixir
Fine-Tuning and Parameter Optimization
• Project: Building a Genetic Algorithm Solution
Defining a Real-World Problem
Designing and Implementing a Solution in Elixir
Testing and Evaluation
• Course Summary and Future Directions
Review of Key Concepts
Exploring Further Applications
Additional Resources and Next Steps in Elixir and AI

Subjects

Conference Talks