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Parallel Problem Solving from Nature session-4

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Introduction to 14 papers in session 4 of PPSN http://home.agh.edu.pl/~ppsn/

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Parallel Problem Solving from Nature session-4

  1. 1. Session 4
  2. 2. View from above
  3. 3. We are all connected
  4. 4. Let's solve the Travelling Posterviewer problem
  5. 5. Careful! Don't touch here!
  6. 6. Paper #190: Evolutionary Detection of New Classes of Equilibria. Application in Behavioral Games D. Dumitrescu et al. <ul><li>People play games in a weird way.
  7. 7. A model has to be found to explain that behavior.
  8. 8. Multiobjective algorithm is used to evolve strategies that approach that behavior </li><ul><li>Which MOEA? </li></ul><li>For those into games </li></ul>
  9. 9. Paper #68: Evolving a Single Scalable Controller for an Octopus Arm with a Variable Number of Segments Woolley & Stanley. <ul><li>Size is not so important </li><ul><li>In terms of #dimensions of state/action space </li></ul><li>A indirectly encoded neurocontroller (HyperNEAT) for an octopus arm is evolved </li><ul><li>Fitness: ability in grasping a target </li></ul><li>A scalable neurocontroller architecture is found.
  10. 10. Next: application to Paul </li></ul>
  11. 11. Paper #104: Evolving Strategies for Updating Pheromone Trails: a Case Study with the TSP Tavares & Pereira <ul><li>Let GP devise your ACO pheromone updating strategy for you.
  12. 12. Weird and interesting update strategies arise </li><ul><li>They generalize better than other update strategies </li></ul><li>Terminal set is key (but not too open to experiments) </li></ul>
  13. 13. Paper #126: Globally Induced Model Trees: An Evolutionary Approach Czajkowski & Krętowski <ul><li>Regression trees useful for extracting patterns in databases </li><ul><li>What are regression trees? </li></ul><li>Usual: top-down approach. Bad. Here: global approach based on EA </li><ul><li>Heuristic mutation/crossover operators used
  14. 14. Akaike information criteria used as fitness </li></ul></ul>
  15. 15. Paper #211: Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics Gibbs et al. <ul><li>Problem: producing a fixture schedule for English football </li><ul><li>Large problem space! </li></ul><li>Solucion: selection hyperheuristics </li><ul><li>Applying heuristics over single candidate solution </li></ul><li>Reinforcement learning rocks! </li></ul>
  16. 16. Paper #162: Evolutionary Learning of Technical Trading Rules without Data-mining Bias. Agapitos et al. <ul><li>Get rich quick! </li><ul><li>Predict performance of trading rules from past behavior. </li><ul><li>Rule performance decays over time </li></ul></ul><li>Grammar-based GP used for rule induction.
  17. 17. MOEA using returns + statistical significance of rule.
  18. 18. Encouraring results! </li></ul>
  19. 19. Paper #208: Differential Evolution Algorithms with Cellular Populations Dorronsoro & Bouvry <ul><li>DE based on updating real-number vectors via combinations of others </li><ul><li>Rarely used in a distributed environment. </li></ul><li>Several DE algorithms tested on a cellular framework.
  20. 20. Cellular better than not (except for JADE) </li></ul>
  21. 21. Paper #70: Hybrid Directional-biased Evolutionary Algorithm for Multi-objective Optimization Shimada et al. <ul><li>Nobody searches the edges of the Pareto front! </li><ul><li>Pareto would be upset with this </li></ul><li>hIDEA adds weights to objectives to allow this. </li><ul><li>Based on IBEA </li></ul><li>(Inverse) General Distance used to evaluate solutions
  22. 22. hIDEA searches wider and closer to Pareto front </li><ul><li>Pareto would be happy! </li></ul></ul>
  23. 23. Paper #80: Solving Multiobjective Optimization Problem by Constraint Optimization Jiang & Zhang & Ren <ul><li>Multiobjective turned into constraint optimization problems </li><ul><li>Multiobjective EA based on constraint optimization: MEACO
  24. 24. n objectives -> n – 1 constraints + 1 objective
  25. 25. COP solution not shown in paper
  26. 26. 2 objectives only? </li></ul><li>Spacing/convergence metrics measured. </li><ul><li>MEACO is the best almost always </li></ul></ul>
  27. 27. Paper #228: Environment-driven Embodied Evolution in a Population of Autonomous Agents Bredeche & Montanier <ul><li>Implicit fitness
  28. 28. Selfish gene hypothesis: strategy successful if it has been able to spread over a population.
  29. 29. Autonomous agents broadcast their genome while alive, load variation of one of those received. </li><ul><li>Implicit selection </li></ul><li>Autonomous agents improve! </li></ul>
  30. 30. Increasing exploration
  31. 31. Paper #131: One-Point Geometric Crossover Alberto Moraglio <ul><li>Crossover can be interpreted geometrically </li><ul><li>And generalized to any data structure where d is defined. </li></ul><li>Crossover results are actually placed over a geodesic between parents.
  32. 32. Crossover operators are proposed for diferent data structures </li><ul><li>And proofs made in flyspeck font </li></ul></ul>
  33. 33. Paper #146: Mirrored Sampling and Sequential Selection for Evolution Strategies Brockhoff & al. <ul><li>Did you know that single-parent non-elitist evolution strategy (ES) can be locally faster than the (1+1)-ES? </li><ul><li>But only with mirrored sampling and sequential selection! </li></ul><li>Convergence speed theoretically computed.
  34. 34. Sequential, mirrored versions of ES and CMA-ES faster and more robust </li></ul>
  35. 35. Paper #120: More Effective Crossover Operators for the All-Pairs Shortest Path Problem Doerr & al. <ul><li>APSP consists in computing all shortests paths betwen pair of nodes in a weighted graph.
  36. 36. Crossover with repair mechanisms improves optimization time.
  37. 37. Feasible parent solution even more: O n 3 log n </li></ul>
  38. 38. Paper #23: General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms Lässig & Sudholt <ul><li>Theoretical analysis of speed-up of parallel evolutionary algorithms on several functions and over different topologies.
  39. 39. Method based on fitness-level methods. </li><ul><li>Finding lower-bound for probability of leaving one level </li></ul><li>Differences abound. </li></ul>
  40. 40. Is source code available? No, it's not! That's not good! Except papers #228 and #146!
  41. 41. Thank you very much Happy postering!

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