Solving Scheduling Problems with Genetic Algorithms in Elixir

Finding the perfect schedule when managing conflicting classes, limited rooms, and credit constraints is a classic optimization challenge. This text-only course guides you through solving these complex allocation problems by building genetic algorithms from scratch in Elixir. You will transition from understanding basic evolutionary concepts to writing a fully functional scheduling engine, learning to model schedules as chromosomes and evaluate them programmatically. What you'll learn: - Understand the core components of genetic algorithms, including populations, chromosomes, and generations. - Model scheduling constraints and academic credits using Elixir structs and pattern matching. - Write robust fitness evaluation functions to score schedules and handle credit limitations. - Implement selection, crossover, mutation, and reinsertion strategies using clean functional code. - Utilize modern Elixir features like MapSet and Streams for efficient data manipulation. - Test your evolutionary operators and fitness logic using the ExUnit framework. The course begins with foundational evolutionary concepts and data modeling before moving step-by-step through the genetic algorithm pipeline. You will read clear explanations, analyze code snippets, and construct a complete scheduling solver. This program is designed for developers who have a basic familiarity with Elixir syntax and want to apply functional programming to real-world optimization problems. Start reading today to master evolutionary algorithms in Elixir.