How One Engineer Used Genetic Algorithms to Optimize API Routing

What this episode covers

Episode 44 of The Software Engineering Podcast with Fexingo: Code, Architecture, and Engineering Best Practices. Lucas and Luna explore the story of a senior backend engineer at a ride-sharing company who used a genetic algorithm to optimize API request routing across hundreds of microservices. The engineer faced a classic NP-hard problem: route requests through a complex dependency graph to minimize latency and cost. Instead of hand-tuning heuristics, they evolved solutions over generations, simulating natural selection. The result: a 40 percent reduction in p99 latency and a 15 percent cut in compute costs, all without changing a single service's logic. Lucas breaks down how genetic algorithms work in practice—fitness functions, crossover, mutation, and convergence criteria—and why this approach beat traditional greedy and round-robin strategies. Luna asks the tough questions: How do you avoid local optima? What about cold-start? And when is it over-engineering? Tune in for a concrete case study that might change how you think about system optimization. #SoftwareEngineering #GeneticAlgorithms #APIRouting #Microservices #Optimization #LatencyReduction #CostOptimization #NPHard #RideSharing #BackendEngineering #EvolutionaryAlgorithms #FitnessFunction #TechPodcast #FexingoBusiness #BusinessPodcast #EngineeringPodcast #CodeArchitecture #BestPractices Keep every episode free: buymeacoffee.com/fexingo

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