Genetik algoritmaların çaprazlama, mutasyon metodlarının ve parametrelerinin gezgin satıcı problemi üzerinde analizi

With the rapid development of whole industry(automotive and especially logistics) and software industry, increasing demand by customers and supply by manifacturers led the optimization more and more important nowadays. By the word for optimization, we mean minimizing production times, maximizing product logictics per transportation or minimizing fuel usage/maximizing fuel saving/efficiency for transportation vehicles. By the demand of these optimizations by the industry, also led optimization algorithms/techniques to grow and evolve. With the evolution of computers and computation powers, classic optimization techniques also evolved. One of evolutinary optimization techniques, Genetic algorithms and genetic programming, corresponded to these heavy demand of optimization area. Basically, genetic algorithms evolved from genetics and applications of sir Charles Darwin, crossover and mutation principles. Using Genetic Algorithms, we have the ability to optimize our solutions for hard problems. Simply, finding/choosing random solutions to the problem and make crossover and mutations on these solutions as the nature does. Crossing over and mutate the parts of solutions by switching the meaningful data between solutions and hope to reach to the best optimized solution. Generally we reach to the optimized solution by finding and trying correct or better crossover and mutation rates. In other words, choosing bad rates for these parameters, most likely leads to worse optimization. In this work, firstly, we presented the genetic algorithms in general way and after that we go in deep and used genetic algorithms to find better optimized results for the famous Traveling Salesman Problem. We chose to apply genetic algorithms on geographical regions of Turkey(Marmara, Aegean and Black Sea regions, 32 cities in total) to find best or best optimized route to travel. While applying genetic algorithms, we modified crossover methods, mutation methods and crossover and mutation rates to reach to the best possible route and analysed final solutions for each used parameter/method and made a comparison between them. Finally, we presented the compared results on graphics to visualize the evolution for each presented parameter. By making these research, we aim to reach out the best or better parameters for real use cases used in the logistics industry to reach better fuel efficiency and reducing fuel costs.