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Bài giảng Máy học nâng cao: Genetic algorithm - Trịnh Tấn Đạt

Bài giảng "Máy học nâng cao: Genetic algorithm" cung cấp cho người học các kiến thức: Introduction - Genetic algorithm (GA), genetic algorithm operators and parameters, example. Cuối bài giảng có phần bài tập để người học ôn tập và củng cố kiến thức. | Bài giảng Máy học nâng cao Genetic algorithm - Trịnh Tấn Đạt Trịnh Tấn Đạt Khoa CNTT Đại Học Sài Gòn Email trinhtandat@sgu.edu.vn Website https sites.google.com site ttdat88 Contents Introduction Genetic Algorithm GA GA Operators and Parameters Example Homework Introduction History Of Genetic Algorithms Evolutionary Computing was introduced in the 1960s by I. Rechenberg. John Holland wrote the first book on Genetic Algorithms Adaptation in Natural and Artificial Systems in 1975. In 1992 John Koza used genetic algorithm to evolve programs to perform certain tasks. He called his method Genetic Programming . What Are Genetic Algorithms GAs GAs are search and optimization techniques based on Darwin s Principle of Natural Selection and Genetic Inheritance. A class of probabilistic optimization algorithms. Widely-used in business science and engineering. Basic Idea Of Principle Of Natural Selection Select The Best Discard The Rest An Example of Natural Selection Rabbits are fast and smart Some of them are faster and smarter than other rabbits. Thus they are less likely to be eaten by foxes. They have a better chance of survival and start breeding. The resulting baby rabbits on average will be faster and smarter Now evolved species are more faster and smarter Genetic Algorithms Implement Optimization Strategies By Simulating Evolution Of Species Through Natural Selection Classes of Search Techniques Search Techniques Calculus Base Guided random search Enumerative Techniques techniques Techniques Fibonacci Sort DFS Dynamic BFS Programming Tabu Search Hill Climbing Simulated Evolutionary Anealing Algorithms Genetic Genetic Programming Algorithms Nature to Computer Mapping GAs use a vocabulary borrowed from nature genetics Nature Computer GAs Population Set of solutions Individuals in environment Solutions to a problem Individual s degree of adaptation to its Solutions quality fitness function surrounding environment Chromosome Encoding for a Solution Gene Part of the .