Research On The Application Of Genetic Algorithms In The Logistics And Distribution Of Trade Games

With the progress of science and technology, we have entered the multidisciplinaryintersecting, permeate scientific era, a typical example is the life science and engineeringcross, permeate and promote each other, as well as modern science and technology to developa distinctive feature.Nowadays, the game industry has entered a period of vigorous development, each gamegame artificial intelligence has become the indispensable one element, if the exquisite2D art,gorgeous3D effect is the game appearance for it, so a good AI design can be seen as the soulof game, in almost all games have a kind of be good to hear or see appearance, the soul isattracted to the core game player.Nowadays, many developers use high quality AI design to make their game in thewhite-hot competition in the market of talent showing itself. Once a year at the gamedevelopers conference, the more developers begin to discuss the game AI design development.The game industry is increasingly recognized AI design for the entire game developmentprocess, the importance of.In recent years, the genetic algorithm (Genetic, Algorithm, GA) become a new randomsearch and optimization algorithm, the basic idea of the theory of evolution based on Darwinand Mendel genetics. Genetic algorithm for solving multi objective and complexity with theunpredictable nature of the problem, there is there is nothing comparable to this superiority.Nineteen nineties, genetic algorithm into the flourishing period of development, boththeoretical research or applied research has become a very popular topic. Genetic algorithm’sapplication research is particularly active, not only to expand its application field, but alsosignificantly improves the use of genetic algorithm optimization and rule learning ability, atthe same time, we also begin to explore the industrial applied research. Genetic algorithm’sapplication research from the beginning of combinatorial optimization expanded into manynewer, more engineering applications.Drop all game content and other factors, trade play a fundamental goal is to make money,how to earn more money, is passed in a city buy cheap goods, and then to the city to sell, thus earning profit.In order to simplify the problem here does not consider the market price fluctuations, andthat each city and special products are the one and only, each product will be in another citywith the highest selling price, so that without considering the warehouse factors under thepremise (that all goods up to purchase1units), as long as to repeatedly traverse all city, will beable to earn maximum profits.So we need to find a AI can traverse all the city ’s path, the path is short, means to spendless time, the unit time profit more, here in order to make the problem more simplified, weignore terrain factors, that all the city can be reached between the straight line, and unitdistance within the time it takes is the same.This paper mainly from four aspects to introduce trade game genetic algorithm in AIapplication, respectively for the genetic algorithm in game development research background,research situation at home and abroad and the main work of this thesis; artificial intelligencetechnology, the game involved in the artificial intelligence technology, in the gamedevelopment in the use of artificial intelligence technology significance now, gamedevelopment in the field of several mainstream AI technology is introduced, mainly including:finite state machine, fuzzy logic, neural network, genetic algorithm. And the future ofartificial intelligence game development direction; genetic algorithm. Genetic algorithm partmainly introduces the genetic algorithm model, mainly includes: the principle of geneticalgorithm, coding technology, group, genetic operation. The characteristic of geneticalgorithm, genetic algorithm and genetic algorithm applicable conditions; introduction totrade the object of the game is simplified,51city TSP specific code design, specific codeoptimization and Simulation of the process and result of51city. Among them,51city TSPspecific code design, mainly introduced: group setting, the design of fitness function,selection operator, crossover operator, mutation operator is designed. In the specific codeoptimization, mainly introduced the size of population, crossover probability, mutationprobability and the choice of the improved crossover operator. While simulating51city in theprocess and result of Traverse City, introduces51improvement effect, improved algorithm insolving a number of other city when performance and trade the game more genetic algorithm.