Genetic Algorithms in Elixir: Build a Tetris AI Agent

Ever wondered how computer programs can learn to play complex games like Tetris without explicit instructions? By combining the power of genetic algorithms with the robust concurrency of Elixir, you can build intelligent systems that evolve and improve over time. This text-only course guides you through the process of modeling evolutionary systems. You will read clear explanations, study structured code examples, and practice writing genetic operators—such as selection, crossover, and mutation—to train an AI agent capable of playing Tetris. What you'll learn: - Understand the core concepts of genetic algorithms, including chromosomes, fitness functions, and evolutionary cycles - Apply Elixir's functional programming patterns and pattern matching to model game states and agent behaviors - Implement selection, crossover, and mutation strategies using clean, maintainable Elixir code - Leverage modern concurrent processing features to evaluate multiple agent generations efficiently - Analyze and track evolutionary progress through structured text-based logs and data outputs You will start with foundational AI and evolutionary definitions before diving into hands-on code. Through step-by-step written guides, you will construct a fully functioning evolutionary pipeline, culminating in an agent that refines its own Tetris-playing strategy. This course is designed for curious programmers and beginners to AI who want to explore evolutionary computation. A basic familiarity with Elixir is helpful, but no prior experience with genetic algorithms is required. Start reading today to build your first evolving AI agent from the ground up.