ga2 - Geneticalgorithms Introduction2...

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Genetic algorithms Introduction-2 satish.chandra@juit.ac.in
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Issues for GA Practitioners Choosing basic implementation issues: representation population size, mutation rate, . .. selection, deletion policies crossover, mutation operators Termination Criteria Performance, scalability Solution is only as good as the evaluation function  (often hardest part)
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When to Use a GA Alternate solutions are too slow or overly complicated Need an exploratory tool to examine new approaches Problem is similar to one that has already been  successfully solved by using a GA Want to hybridize with an existing solution
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Example 0 or 1 represents a fixed bit Asterisk represents a “don’t care” 11****00 is the set of all solutions encoded in 8 bits, beginning with  two ones and ending with two zeros Solutions in this set all share the same variants of the properties  encoded at these loci
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Schema qualifiers Length The inclusive distance between the two bits in a schema which are  furthest apart (the defining length of the previous example is 8) Order The number of fixed bits in a schema (the order of the previous  example is 4) 
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Why does GA work? Schemas can be destroyed or conserved So how are good schemas propagated through generations? Conserved – good –  schemas confer higher fitness on the  offspring inheriting them Fitter offspring are probabilistically more likely to be chosen to  reproduce  
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Theoretical Foundation Lemmas to the Schema Theorem Selection focuses the search Crossover combines good schemas Mutation is the insurance policy
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Example Example Problem Schedule  n  jobs on  m  processors such that the  maximum span is minimized. Design alternative
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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ga2 - Geneticalgorithms Introduction2...

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