Solving the Traveling Salesman Problem with Elixir and GA

Anders Björkland writes about applying genetic algorithms in Elixir to solve the Traveling Salesman Problem (TSP), a classic optimization challenge. He begins by contemplating the inefficient routes taken by delivery trucks as inspiration to solve TSP. He introduces the concept of TSP and describes various approaches to it, including brute force, nearest neighbor, and genetic algorithms, the last one being the focus of the article. Björkland outlines a plan involving chromosomes, fitness functions, parent selection, crossover, mutation, and pruning to simulate evolutionary processes. He details Elixir code implementations for each step, explaining how a chromosome of city indices represents a potential solution that evolves over generations to find a substantially optimal travel route. The article discusses different scenarios with varying population sizes and parent ratios, demonstrating how these factors influence the efficiency of the genetic algorithm in improving solutions over time. Björkland concludes by reflecting on the fun and educational aspects of working with genetic algorithms and Elixir.