Lecture14-2-25-2002

Lecture14-2-25-2002 - MAE 552 Heuristic Optimization...

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MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #14 2/25/02 Evolutionary Algorithms
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Practical Implementation Issues The next set of slides will deal with some issues encountered in the practical implementation of a genetic algorithm. Specifically constraint handling and convergence criteria.
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Constraint Handling What if we have a constrained problem? Typically would use a penalty function to worsen the fitness of any designs which violate constraints.
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Constraint Handling Example of a penalty term. This will provide a numerical violation value. It is up to you to decide how this will effect your fitness. = = + = m j l k k j x h x g x P 1 1 2 2 ) ( ] 0 ), ( max[ ) (
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Convergence Common ways of determining convergence: 1. No change in best quality design over chosen number of gens. 2. No change in average quality of population of chosen number of gens. 3. Set a maximum allowable number of generations. 4. Set a maximum allowable number of objective function evaluations.
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Putting It All Together begin t = 0 initialize P(t) evaluate P(t) while (not converged) do t = t + 1 select P(t) from P(t-1) alter P(t) (variation operators) evaluate P(t) end do while end
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This note was uploaded on 07/09/2011 for the course MAE 522 taught by Professor Hacker during the Spring '10 term at SUNY Buffalo.

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Lecture14-2-25-2002 - MAE 552 Heuristic Optimization...

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