Building a Tetris AI Agent with Genetic Algorithms in Elixir

How do you teach a computer to play a puzzle game without explicitly programming the rules of strategy? By combining the power of genetic algorithms with the clean, concurrent nature of Elixir, you can simulate evolution to breed highly effective game-playing agents. This text-based course guides you through the process of modeling game states and running evolutionary simulations from scratch. By the end of this course, you will understand how to represent game boards, define evaluation heuristics, and run a complete evolutionary loop to optimize your agent's decision-making. You will gain a practical grasp of both functional programming patterns and foundational AI concepts. What you'll learn: - Understand the core concepts of genetic algorithms, including chromosomes, selection, crossover, and mutation - Model Tetris game states and board representations using Elixir's immutable data structures - Design effective fitness functions that evaluate and score the quality of different game moves - Implement the evolutionary loop in Elixir to continuously breed smarter game-playing strategies - Leverage Elixir's pattern matching and functional pipelines to write clean, testable AI logic - Practice structuring evolutionary parameters to balance exploration and exploitation We begin with foundational definitions, exploring how genetic algorithms mimic natural selection and why Elixir's functional paradigm is ideal for state manipulation. From there, you will read through step-by-step code explanations to construct the game representation, the scoring heuristics, and the evolutionary engine. This course is designed for software developers and curious programmers who want to explore AI concepts using Elixir. No prior background in artificial intelligence or advanced mathematics is required. Start reading today to build your own evolutionary AI agent from the ground up.