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s02lec24 - 15.053 Tuesday, May 14 The basic genetic...

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1 15.053 Tuesday, May 14 z Genetic Algorithms Handouts: Lecture Notes Question: when should there be an additional review session? 2 The basic genetic algorithm z Developed by John Holland in 1975 z Simulates the process of evolution z Basic Principle: Evolution can be viewed as an optimizing process 3 More on physical analogies z Physical Analogies as a guiding principle for optimization problems Genetic Algorithms John Holland 1975 Simulated Annealing Kirkpatrick Ant Colony Systems 4 The basic genetic algorithm z Loosely modeled on natural selection with a touch of molecular biology thrown in. z Fitter individuals mate [selection operator]. z The chromosomes of each child are formed as a mixture of the chromosomes of the parents [crossover operator]. z Mutation adds diversity within the species and a greater scope for improvement [mutation operator]. z Chromosomes encode the relevant information. 5 GA terms chromosome gene alleles 1 or 0 selection crossover mutation population (solution) (variable) (values) Objective: maximize fitness function (objective function) 6 Selection Operator : Selects two parents from the population for mating. The selection is biased towards fitter individuals. Crossover Operator : Each child is obtained as a random mixture of its parents using a crossover operation. Mutation Operator : At times an individual in the population undergoes a random mutation.
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7 A Simple Example: Maximize the number of 1’s z Initial Population Fitness z 1 1 1 0 1 4 z 0 1 1 0 1 3 z 0 0 1 1 0 2 z 1 0 0 1 1 3 z Average fitness 3 Usually populations are much bigger, say around 50 to 100, or more. 8 Crossover Operation: takes two solutions and creates a child (or more) whose genes are a mixture of the genes of the parents. 0 1 1 0 1 parent 1 1 0 0 1 1 parent 2 Select two parents from the population. This is the selection step. There will be more on this later. 9 Crossover Operation: takes two solutions and creates a child (or more) whose genes are a mixture of the genes of the parents. 0 1 1 0 1 1 0 0 1 1 parent 1 parent 2 0 1 1 1 1 child 1 1 0 0 0 1 child 2 1 point crossover : Divide each parent into two parts at the same location k (chosen randomly.) Child 1 consists of genes 1 to k-1 from parent 1 and genes k to n from parent 2. Child 2 is the “reverse”.
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This note was uploaded on 12/20/2011 for the course BUS 15.053 taught by Professor Prof.jamesorlin during the Spring '05 term at MIT.

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s02lec24 - 15.053 Tuesday, May 14 The basic genetic...

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