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Updates

Updates. September 24 th , 2013 Erik Fredericks. Overview. Updates from previous meeting Literature review on local optima. Updates. Removed incremental evaluation of pre/post conditions Left in check for valid/invalid transforms

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Updates

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  1. Updates September 24th, 2013 Erik Fredericks

  2. Overview • Updates from previous meeting • Literature review on local optima

  3. Updates • Removed incremental evaluation of pre/post conditions • Left in check for valid/invalid transforms • Added in secondary fitness function to increase depth of tree • Also increased maximum tree depth • Reintroduced crossover • Mutation rate: 25% • Crossover rate: 50%

  4. Results • Moving away from proper solutions • Invalid transformation chains • ARR2INDEX(ARR2INDEX(ARR2INDEX… • VOID2INDEX(float-array) • Appears to be a flaw in code, not in approach • Still hunting this bug down

  5. Results • Diversity is back from crossover operations • Generational run takes much longer now than before • Cannot yet comment on performance of algorithm until glitch is fixed

  6. Local Optima Paper Reviews • Novelty search in GE [Urbano2013] • Get out of local optima in Santa Fe Ant Trail problem • Deceptive problem • No archive added as experiments showed it did not help • Results show that novelty search outperforms standard GE

  7. Local Optima Paper Reviews • Other GE approach • Grammatical herding [Headleand2013] • Swarm-based heuristic • Treats environment as solution space and ‘herds’ solutions towards high-fitness areas • Contains: • Herd – standard population of individuals • Betas – subset of fittest agents to drive herd based on location/fitness • Alphas – Betas with highest fitness • Algorithm ‘seeded’ with individuals evolved with GH, and then optimized with standard GE • Typically able to converge to a solution (Santa Fe Ant Trail problem)

  8. Seeded Grammatical Evolution

  9. GE Crossover • GE crossover found to be ‘destructive’ [O’Neill2003] • One-point crossover (standard crossover algorithm) • Destroys good trees and generates bloat • Exploration of biological-inspired crossovers • Homologous • Headless-chicken • Ripple

  10. GE Crossover • Homologous • History of rules for each grammar stored and aligned • Read sequentially and region of similarity noted • First crossover points selected as boundary for region of similarity • Second from region of dissimilarity • Two-point crossover performed • Results • Standard one- and two-point crossover tend to be more consistent

  11. GE Crossover • Headless chicken • Select fragments for crossover • Replaces with randomly-generated bit strings of same length • Results • Standard one-point crossover performs far better • System runs better with crossover switched off

  12. GE Crossover • Ripple • Map codons from middle of parse tree instead of left (preorder traversal) side • Find ‘ripple points’ • One or more sub-trees that can be removed • Points on one sub-tree can encode an entirely different sub-tree on another ripple point • Results • Performs well • Tends to search a more global space

  13. Meeting Schedule Proposal • Proposed update to meeting schedule • Move to meeting twice a month, with an email update in the off week • Due to limited amount of available weekly development time, this may be a more efficient method to make progress • Can schedule interim meetings if discussion / review is necessary in off-weeks

  14. Related Work • Improving Grammatical Evolution in Santa Fe Trail using Novelty Search • Urbano and Georgiou, ECAL 2013 • Swarm Based Population Seeding of Grammatical Evolution • Headleand and Teahan, Journal of Computer Science and Systems Biology 2013 • Crossover in Grammatical Evolution • O’Neill, Genetic Programming and Evolvable Machines 2003

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