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Heuristics and Genetic Algorithms

Heuristics and Genetic Algorithms. Michael D. Mobley The Boeing Company 12 July 2006. Heuristics and Genetic Algorithms. Optimal Solutions Interest Systems Engineers Up-Front Trade Studies Resource Selection Problems Air Attack Resources to Prosecute Evolving Targets Available Fuel

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Heuristics and Genetic Algorithms

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  1. Heuristics and Genetic Algorithms Michael D. Mobley The Boeing Company 12 July 2006

  2. Heuristics and Genetic Algorithms • Optimal Solutions Interest Systems Engineers • Up-Front Trade Studies • Resource Selection Problems • Air Attack Resources to Prosecute Evolving Targets • Available Fuel • Available Number & Types of Weapons • Current Aircraft Locations • Current Target Priority Rules of Engagement • Systems architecting itself is a search process based on systems measures of effectiveness or attributes

  3. Heuristics and Genetic Algorithms • Function to Be Optimized Might Look Like This

  4. Heuristics and Genetic Algorithms • Genetic Algorithms Search the Architecture Alternative Space Using These Components... • Genetic Representation of Problem • Method to Create Initial Population of Solutions • Evaluation Function to Rate Fitness of Solutions • Genetic Operators to Alter “Children” • Values for Parameters

  5. Heuristics and Genetic Algorithms • Heuristic • Literally, “To Find a Way” or “To Guide” • Help to Reduce the Search Space

  6. Heuristics and Genetic Algorithms • This Presentation Will Not… • Be an Exhaustive Discussion of How Genetic Algorithms Work • Discuss All Potentially Useful Heuristics • What We Will Do… • Examine Similarities Between Genetic Algorithms and Heuristic Approaches • Explore a Hybrid Approach to Systems Architecting Using Genetic Algorithms and Heuristics

  7. Heuristics and Genetic Algorithms • Similarities Between the Two Approaches • Intimate Involvement with the Search Space • Heuristics Limit the Space • Genetic Algorithms Search the Space • Isolated Use Discouraged • Neither One by Themselves May Get the Optimal Solution • Both Together May Get Optimal Solution Faster • Iterative in Nature • Genetic Algorithms are Recursive • The First Heuristic Used May Not Significantly Reduce the Search Space

  8. Heuristics and Genetic Algorithms • A Hybrid Approach • For Each Genetic Algorithm Component… • Identify One or More Heuristics that Apply

  9. Heuristics and Genetic Algorithms • Genetic Representation of the Problem • Don’t assume that the original statement of the problem is necessarily best, even the right one. • Sometimes the best way to defeat a system is to do so “out of bounds.” It may also be the best way to preserve it.

  10. Heuristics and Genetic Algorithms • Function to Be Optimized Might Look Like This

  11. Heuristics and Genetic Algorithms • Method to Create Initial Population of Solutions • When choices must be made with unavoidably inadequate information, choose the best available and then watch to see whether future solutions appear faster than future problems. If so, the choice was at least adequate. If not, go back and choose again.

  12. Heuristics and Genetic Algorithms • Method to Create Initial Population of Solutions • Traveling Salesman Problem • Initial “Parent” Solutions • [123456789] and [546921783]

  13. Heuristics and Genetic Algorithms • Evaluation Function to Rate Fitness of Solutions • No complex system can be optimum to all parties concerned, nor all functions optimized. • Regardless of what has gone before, the acceptance criteria determine what is actually built.

  14. Heuristics and Genetic Algorithms • Genetic Operators to Alter “Children” • The efficient architect, using contextual sense, continually looks for the likely misfits and redesigns the architecture so as to eliminate or minimize them.

  15. Heuristics and Genetic Algorithms • Genetic Operators to Alter “Children” • Traveling Salesman Problem • Create Offspring by Swapping Chromosomes in Positions Three through Six • [126921789] and [543456783] • Deal with Duplicate Children • [356921784] and [293456781]

  16. Heuristics and Genetic Algorithms • Values for Parameters • For a system to meet its acceptance criteria to the satisfaction of all parties, it must be architected, designed, and built to do so—no more and no less. • Different architectures can generate different behavior.

  17. Heuristics and Genetic Algorithms • Application to Your Problem • What Do You Wish to Optimize? • What Constraints Bound Your Problem? • How Can Your Problem Be Represented by Genetic Algorithm? • What Heuristics Can Aid the Genetic Algorithm? • What Is Your Optimal Solution?

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