Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Phenotype to Genotype Matching and Epigeneticsin Evolutionary Algorithms

William Maroney

Australian National University

u5612989

May 21, 2017

Supervisors: Tom Gedeon and Bob McKay

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Overview

Evolutionary computing background

Research motivation

Possible epigenetic models for the genetic algorithm

Experimental design and findings

Future work

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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 givencandidate solution?

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Interactive Evolutionary Computing (IEC)

Evaluation of the fitnessfunction requires humaninput

Example: how much doyou like this? Rate it.

Computing power doesn’thelp us . . .

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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 simplisticmodel of the theory of evolution. Does a moreaccurate/comprehensive representation improve things?

Consider epigenetics?

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Possible Epigenetic Models

Traditional genetic algorithm: assign whole fitness valuef (g) := f (m∗(g))

Epigenetics (exact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p | g)× 1

||m∗(g)−p|| (all evaluated p, time ≤ τ)

Epigenetics (inexact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p′ | g)× 1

||p−p′|| ×1

||m∗(g)−p|| (closet p′ to p)

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Possible Epigenetic Models

Traditional genetic algorithm: assign whole fitness valuef (g) := f (m∗(g))

Epigenetics (exact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p | g)× 1

||m∗(g)−p|| (all evaluated p, time ≤ τ)

Epigenetics (inexact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p′ | g)× 1

||p−p′|| ×1

||m∗(g)−p|| (closet p′ to p)

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Possible Epigenetic Models

Traditional genetic algorithm: assign whole fitness valuef (g) := f (m∗(g))

Epigenetics (exact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p | g)× 1

||m∗(g)−p|| (all evaluated p, time ≤ τ)

Epigenetics (inexact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p′ | g)× 1

||p−p′|| ×1

||m∗(g)−p|| (closet p′ to p)

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Possible Epigenetic Models

Traditional genetic algorithm: assign whole fitness valuef (g) := f (m∗(g))

Epigenetics (exact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p | g)× 1

||m∗(g)−p|| (all evaluated p, time ≤ τ)

Epigenetics (inexact P matches): proportionally infer fitnessf τ (g) :=

∑p f (p)× p(p′ | g)× 1

||p−p′|| ×1

||m∗(g)−p|| (closet p′ to p)

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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 modelModel 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

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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 epigeneticmodel fitness functionConsider MAE per generation

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Results

Experiments are suggestive, not conclusive

Unclear that epigenetic fitness inference increases convergence

However, fitness values are affected

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Experimental Results

All models produce non-random results (i.e. consistentlybetter than“indifferent” rating)

Hard to infer if any model is clearly superior

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

Experimental Results

Some effect on fitness values ⇒ altered selection

Actual impact and importance still unclear

Phenotype to Genotype Matching and Epigenetics in Evolutionary Algorithms

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 functionhyper-parameters (i.e. scale and normalisation factors)