Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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1 Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms William Maroney Australian National University u May 21, 2017 Supervisors: Tom Gedeon and Bob McKay

2 Overview Evolutionary computing background Research motivation Possible epigenetic models for the genetic algorithm Experimental design and findings Future work

3 The Genetic Algorithm An optimisation technique Useful for large, complex, or undefined search spaces Uses theory of evolution: survival of the fittest Requires fitness function: how good is a given candidate solution?

4 Interactive Evolutionary Computing (IEC) Evaluation of the fitness function requires human input Example: how much do you like this? Rate it. Computing power doesn t help us...

5 Research motivation Can we improve performance of the genetic algorithm? Can we reduce the impact of humans in IEC? i.e. find good solutions faster The genetic algorithm works pretty well with a simplistic model of the theory of evolution. Does a more accurate/comprehensive representation improve things? Consider epigenetics?

6 Possible Epigenetic Models Traditional genetic algorithm: assign whole fitness value f (g) := f (m (g)) Epigenetics (exact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 m (all evaluated p, time τ) (g) p Epigenetics (inexact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 p p 1 m (g) p (closet p to p)

7 Possible Epigenetic Models Traditional genetic algorithm: assign whole fitness value f (g) := f (m (g)) Epigenetics (exact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 m (all evaluated p, time τ) (g) p Epigenetics (inexact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 p p 1 m (g) p (closet p to p)

8 Possible Epigenetic Models Traditional genetic algorithm: assign whole fitness value f (g) := f (m (g)) Epigenetics (exact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 m (all evaluated p, time τ) (g) p Epigenetics (inexact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 p p 1 m (g) p (closet p to p)

9 Possible Epigenetic Models Traditional genetic algorithm: assign whole fitness value f (g) := f (m (g)) Epigenetics (exact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 m (all evaluated p, time τ) (g) p Epigenetics (inexact P matches): proportionally infer fitness f τ (g) := p f (p) p(p g) 1 p p 1 m (g) p (closet p to p)

10 Experimental Set-up Multiple test subjects Each test subject evaluated all three models GA model, epigenetic model (exact and inexact matches) Same number of generations for each model Model order assigned to avoid possible bias Consistent hyper-parameters (i.e. only compare models) Need to avoid user fatigue limited fitness evaluations Unavoidable challenge with IEC

11 Performance Measures Hypothesis: epigenetic fitness inference increases convergence Hypothesis: epigenetic fitness inference affects convergence Ratio of positive artworks to all artworks over time Identify affect on fitness values - Mean Absolute Error (MAE) Compare genetic model fitness function to each epigenetic model fitness function Consider MAE per generation

12 Results Experiments are suggestive, not conclusive Unclear that epigenetic fitness inference increases convergence However, fitness values are affected

13 Experimental Results All models produce non-random results (i.e. consistently better than indifferent rating) Hard to infer if any model is clearly superior

14 Experimental Results Some effect on fitness values altered selection Actual impact and importance still unclear

15 Future Work Further investigation into experimental implications Increase scale of experiments performed in this work Apply epigenetic models to other problems (IEC, and EC) More investigation into epigenetic fitness function hyper-parameters (i.e. scale and normalisation factors)