صفحه محصول - پاورپوینت Genetic Algorithms and Protein Folding

پاورپوینت Genetic Algorithms and Protein Folding (pptx) 23 اسلاید

دسته بندی : پاورپوینت

نوع فایل : PowerPoint (.pptx) ( قابل ویرایش و آماده پرینت )

تعداد اسلاید: 23 اسلاید

قسمتی از متن PowerPoint (.pptx) :

Genetic Algorithms and Protein Folding Genetic Algorithm: is a heuristic method that operates on pieces of information like nature does on genes in the course of evolution. Individuals are represented by a linear string of letters of an alphabet (in nature nucleotides, in genetic algorithms bits) Individuals are allowed to mutate, crossover and reproduce. Fitness function evaluates individuals. Depending on the generation replacement mode a subset of parents and offspring enters the next reproduction cycle. After a number of iterations the population consists of individuals that are well adapted in terms of the fitness function. It cannot be proven that the individuals of a final generation contain an optimal solution for the objective encoded in the fitness function. Initialise a population of individuals. This can be done either randomly or with domain specific background knowledge to start the search with promising seed individuals. (Where available the latter is always recommended. ) Individuals are represented as a string of bits. A fitness function must be defined that takes as input an individual and returns a number (or a vector) that can be used as a measure for the quality (fitness) of that individual. The application should be formulated in a way that the desired solution to the problem coincides with the most successful individual according to the fitness function. II. Evaluate all individuals of the initial population. III. Generate new individuals. The reproduction probability for an individual is proportional to its relative fitness within the current generation. Crossover two point crossover 0101001111000011010101011110111 1010101101011100101110001010101 uniform crossover 0101001111000011010101011110111 1010101101011100101110001010101 Genetic Operators: Mutation. Substitute one or more bits of an individual randomly by a new value (0 or 1). Variation. Change the bits in a way that the number encoded by them is slightly incremented or decremented. Crossover. Exchange parts (single bits or strings of bits) of one individual with the corresponding parts of another individual. Originally, only one-point crossover was performed but theoretically one can process up to L - 1 different crossover sites (with L as the length of the individual).                                                                                                                                                                                                  IV. Select individuals for the new parent generation. Schemes: 1) Complete offspring is selected while all parents are discarded (original genetic algorithm). This is motivated by the biological model and is called total generation replacement. 2) The n best individuals (from old and new generation) This method is called elitist generation replacement. V. Go back to step 2 until either a desired fitness value was reached or until a predefined number of iterations was performed Init the first generation Representation Formalism hybrid approach - genetic algorithm is configured to operate on numbers, not bit strings as in the original genetic algorithm. Disadvantages: the mathematical foundation of genetic algorithms holds only for binary representations, although some of the mathematical properties are also valid for a floating point representation. Binary representations run faster in many applications. An additional encoding/decoding process may be required to map numbers onto bit strings.