Lecture-24-%2BGenetic%2BAlgorithm-II - Evolutionary...

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Evolutionary Algorithms 1 Genetic Algorithms II Zain Iqbal 5/31/17
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GENETIC ALGORITHM Basic Attributes of Genetic Algorithm 1. Genetic representation of candidate solutions 2. Population size 3. Fitness (Evaluation) function 4. Genetic Operators 5. Selection algorithm 6. Generation gap 7. Amount of elitism used 8. Number of duplicates allowed 2
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GENETIC ALGORITHM 1. Genetic Representation of Candidate Solutions Chromosomes have either binary or real valued genes In binary coded chromosomes, every gene has two alleles In real coded chromosomes, a gene can be assigned any value from a domain of values 3
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GENETIC ALGORITHM 4 2. Population Size Number of individuals present and competing in an iteration (generation) If the population size is too large , the processing time is high and the GA tends to take longer to converge upon a solution (because less fit members have to be selected to make up the required population) If the population size is too small , the GA is in danger of premature convergence upon a sub-optimal solution (all chromosomes will soon have identical traits). This is primarily because there may not be enough diversity in the population to allow the GA to escape local optima
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GENETIC ALGORITHM 3. Evaluation/Fitness Function It is used to determine the fitness of a chromosome Creating a good fitness function is one of the challenging tasks of using GA 5
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that are 6 GENETIC ALGORITHM 4. Genetic Operators Genetic operators areapplied to chromosomes selected to be parents, to create offspring Basically of two types: Crossover and Mutation Crossover operators create offspring by recombining the chromosomes of selected parents Mutation is used to make small random changes to a chromosome in an effort to add diversity to the population
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GENETIC ALGORITHM Genetic Operators: Crossover Two types: Single point crossover & Uniform crossover Single type crossover This operator takes two parents and randomly selects a single point between two genes to cut both chromosomes into two parts (this point is called cut point) The first part of the first parent is combined with the second part of the second parent to create the first child The first part of the second parent is combined with the second part of first parent to create the second child 7 100 0010 111 0001 1000001 1110010
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GENETIC ALGORITHM Genetic Operators: Crossover Uniform crossover The value of each gene of an offspring’s chromosome is randomly taken from either parent This is equivalent to multiple point crossover 8 1000010 1110001 1 0 10 01 0
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GENETIC ALGORITHM Genetic Operators: Mutation Each gene of each offspring is mutated with a given mutation rate p (say 0.01) It is hence possible that no gene may be mutated for many generations. On the other hand more than one gene may be mutated in the same generation (or even in the same chromosome) For real valued genes , the value is selected randomly from the alleles If the rate is too low, new traits will appear too slowly in the population. If the rate is too high, each generation will be unrelated to the previous generation 9
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Selection Algorithms
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