Ec awards lecture spring 2008 advances in parameterless evolutionary algorithms
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EC Awards Lecture ~ Spring 2008 Advances in Parameterless Evolutionary Algorithms. Lisa Guntly André Nwamba Research Advisor: Dr. Daniel Tauritz Natural Computation Laboratory. Evolutionary Algorithms (EAs). User Parameters. Problem. Evolutionary Algorithm (EA). Solution.

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EC Awards Lecture ~ Spring 2008 Advances in Parameterless Evolutionary Algorithms

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EC Awards Lecture ~ Spring 2008Advances in Parameterless Evolutionary Algorithms

Lisa Guntly

André Nwamba

Research Advisor: Dr. Daniel Tauritz

Natural Computation Laboratory


Evolutionary Algorithms (EAs)

User Parameters

Problem

Evolutionary Algorithm (EA)

Solution


Evolutionary Algorithms

Create Initial Population

Evaluate Fitness

No

Select Parents

Termination

Create Offspring

Yes

Solution

Select Survivors

Evaluate Fitness


Motivation

  • Parameter specification complicates EAs

    • Expert knowledge required

    • Time-consuming

    • Sub-optimal - optimal parameter values can change during a run


The Effects of Parameter Values


Parameterless EAs: Our Approach

  • Completely Parameterless EAs

  • Biological metaphors may be useful

  • Typical parameters:

    • Population size

    • Parent selection operators

    • Offspring size

    • Survival selection

    • Mutation operators

    • Crossover operators


Futility-Based Offspring Sizing (FuBOS)

André Nwamba


FuBOS: Futility-Based Offspring Sizing

  • Minimize wasted computation effort


Approach

  • Look at change in average fitness of the offspring

  • Average fitness of all n offspring

  • Average fitness of n-1 previously created offspring

  • Threshold value


Experimental Setup

  • Compared FuBOS-EA and manually tuned EA (OOS-EA)

  • FuBOS-EA uses ε=.001

  • Test problems: DTRAP, SAT, and ONEMAX

  • Used population sizes of 100, 500, 1000

  • All tests used same parameters

  • Performance compared using One-Way ANOVA with significance level of .05


Results


Results


Results


Results


Results


Conclusions

  • Competitive performance

  • Extra parameter


FuBOS Future Work

  • The “epsilon problem”

  • Genetic Diversity

  • Parent Selection

  • Combine with dynamic population sizing


Age-Based Population Sizing (ABPS)

Lisa Guntly


The Importance of Age

  • Age significantly impacts survival in natural populations


F

i

S

S

i

AGE

F

B

Methods

  • Survival chance (Si) of an individual is based on age and fitness

  • Main Equation

Fitness of i

=

Best Fitness


S

R

(

AGE

)

AGE

A

Survival Chance from Age

  • Age is tracked by individual, and is incremented every generation

  • Two equations explored for SAGE

  • Equation 1 (ABPS-EA1): linear decrease

1

=

-

Rate of decrease from age


N

AG

S

AGE

Survival Chance from Age (cont’d)

  • Equation 2 (ABPS-EA2): more dynamic

Number of individuals in the same age group

AGE

1

=

-

-

2

P

2

G

Population size

Number of generations the EA will run


N

AGE

=

-

-

AG

S

1

AGE

2

P

2

G

Survival Chance from Age (cont’d)

  • Effects of

    • More individuals of the same age will decrease their survival chance

    • Age will decrease survival chance relative to the maximum age (G)

NAG

Si


Experimental Setup

  • Testing done on TSP (size 20/40/80)

  • Offspring size is constant

  • Compared to a manually tuned EA

  • Examine effects of

    • Initial population size

    • Offspring size

  • Tracked population statistics

    • Size

    • Average age

    • Global best fitness (GBF)


Performance Results - TSP size 20

Average over 30 runs

Global best fitness

ABPS-EA1 -

ABPS-EA2 -


Performance Results - TSP size 40

Average over 30 runs

Global best fitness

ABPS-EA1 -

ABPS-EA2 -


Initial Population Size Effect

3 different runs


Tracking Population Size and Average Age

Same single run


F

F

F

i

i

W

S

S

AGE

AGE

F

F

F

B

B

W

Equation with Fitness Scaling

  • Attempt to fix the lack of selection pressure from fitness

  • New Main Equation

Fitness of i

-

Fitness Scaling

S

S

=

=

i

i

-

Best Fitness

Worst Fitness


Initial Performance Analysis from Fitness Scaling Equation

Average over 30 runs

using

Global best fitness


Initial Performance Analysis from Fitness Scaling Equation (cont’d)

  • Independence from initial population size was maintained

  • Dynamic adjustment of population size during the run was improved

  • Additional selection pressure from elitism improved performance slightly


ABPS Conclusions

  • Independence from initial population value was achieved

  • Autonomous adjustment of population size during a single EA run was successful

  • Fitness scaling is needed for ABPS to work on more difficult problems


ABPS Future Work

  • Further exploration of fitness scaling methods

  • Test on other difficult problems

  • Compare to other dynamic population sizing schemes

  • Implement age-based offspring sizing


Impact


Impact

  • Increases industry usability

  • Higher performance EAs

  • Progress towards completely parameterless EA


Questions?


FuBOS Experimental Setup


Experimental Setup

  • DTRAP

  • SAT

  • ONEMAX


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