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Introduction to Evolutionary Algorithms

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Introduction to Evolutionary Algorithms

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Introduction to Evolutionary Algorithms

  1. 1. Can we steal the techniques used in nature to solve problems?
  2. 2. Introduction toEvolutionary Algorithms (and open questions) Herb Susmann Computer Science ‘14 Advisor: Dr. Gregg Hartvigsen
  3. 3. In Three Parts: Introduction Examples My Research
  4. 4. WHAT IS A GENETICALGORITHM?
  5. 5. A genetic algorithm is a biologically inspired optimization algorithm.
  6. 6. Cultural Algorithm Swarm AlgorithmsMemetic Algorithm Particle Swarm OptimizationProbabilistic Algorithms Ant SystemPopulation-Based Incremental Learning Ant Colony SystemUnivariate Marginal Distribution Algorithm Bees AlgorithmCompact Genetic Algorithm Bacterial Foraging Optimization AlgorithmBayesian Optimization AlgorithmCross-Entropy Method Immune Algorithms Clonal Selection AlgorithmEvolutionary Algorithms Negative Selection AlgorithmGenetic Algorithm Artificial Immune Recognition SystemGenetic Programming Immune Network AlgorithmEvolution Strategies Dendritic Cell AlgorithmDifferential EvolutionEvolutionary Programming Neural AlgorithmsGrammatical Evolution PerceptronGene Expression Programming Back-propagationLearning Classifier System Hopfield NetworkNon-dominated Sorting Genetic Algorithm Learning Vector QuantizationStrength Pareto Evolutionary Algorithm Self-Organizing Map Source: cleveralgorithms.com
  7. 7. Biological Evolution: Natural SelectionGenetic Recombination & Mutation
  8. 8. How can we model this in a computer?
  9. 9. A very informal description:1. Generate a population of random individuals2. Kill off the worst individuals in the population Selection Pressure3. Let the good individuals mutate and recombine to replace the bad ones Genetic Recombination4. Repeat until ending criteria met
  10. 10. A very informal example:Evolving the colorBLUE
  11. 11. Red Green Blue209 232 3522 88 2062 189 28117 187 40204 28 225184 156 17627 70 161174 171 176
  12. 12. Red Green Blue209 232 3522 88 2062 189 28117 187 40204 28 225184 156 17627 70 161174 171 176
  13. 13. Red Green Blue22 88 16122 88 20627 88 20627 70 16127 88 16122 88 16127 70 16127 88 161
  14. 14. Red Green Blue 22 88 161MUTATE 2 22 88 206 Adaption Red -20 27 88 206 27 70 161 27 88 161MUTATE 22 119 88 161 DeleteriousGreen +31 27 70 161 27 88 161
  15. 15. Example:DATA FITTING
  16. 16. Growth Rate Carrying Capacity
  17. 17. Starting Population Size
  18. 18. How can we rank them? Sum of the error squared
  19. 19. Free R CodeHartvigsen’s OutboxfilesOutBoxBiologyhartvigShared Learning in ScienceMy Websitehttp://herbsusmann.com
  20. 20. DEMONSTRATION
  21. 21. Final Note:There are much better algorithms to do this.
  22. 22. MY RESEARCH
  23. 23. Evolve Mathematical Disease Models to fit Data
  24. 24. Susceptible InfectiousRecovered
  25. 25. Can we go the other direction?
  26. 26. Cultural Algorithm Swarm AlgorithmsMemetic Algorithm Particle Swarm OptimizationProbabilistic Algorithms Ant SystemPopulation-Based Incremental Learning Ant Colony SystemUnivariate Marginal Distribution Algorithm Bees AlgorithmCompact Genetic Algorithm Bacterial Foraging Optimization AlgorithmBayesian Optimization AlgorithmCross-Entropy Method Immune Algorithms Clonal Selection AlgorithmEvolutionary Algorithms Negative Selection AlgorithmGenetic Algorithm Artificial Immune Recognition SystemGenetic Programming Immune Network AlgorithmEvolution Strategies Dendritic Cell AlgorithmDifferential EvolutionEvolutionary Programming Neural AlgorithmsGrammatical Evolution PerceptronGene Expression Programming Back-propagationLearning Classifier System Hopfield NetworkNon-dominated Sorting Genetic Algorithm Learning Vector QuantizationStrength Pareto Evolutionary Algorithm Self-Organizing Map Source: cleveralgorithms.com
  27. 27. Initial results: Does well if giventhe parameter values.
  28. 28. Next Step:Embed a differential genetic algorithm to evolve parameter values.
  29. 29. This is an open question, I want your ideas!
  30. 30. I want to collaborate with you!
  31. 31. Special Thanks to: Dr. Gregg HartvigsenThe Distributed Systems Lab & Prof. Homma FarianThe open source community!
  32. 32. Questions?

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