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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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September 24th, 2013

Erik Fredericks

  • Updates from previous meeting
  • Literature review on local optima
  • 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%
  • Moving away from proper solutions
    • Invalid transformation chains
      • VOID2INDEX(float-array)
  • Appears to be a flaw in code, not in approach
    • Still hunting this bug down
  • 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
local optima paper reviews
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
local optima paper reviews1
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)
ge crossover
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
ge crossover1
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
ge crossover2
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
ge crossover3
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
meeting schedule proposal
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
related work
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