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Age-Based Population
Dynamics in Evolutionary
Algorithms
Lisa Guntly
Motivation
• Parameter specification complicates
EAs
• Optimal parameter values can change
during a run
• Age is an important factor in Biology
The Importance of Age
• Age significantly impacts survival in
natural populations
Methods
• Survival chance (Si) of an individual is
based on age and fitness
• Main Equation
Fitness of i
Fi
Si =
SAGE
FB
Best Fitness
Survival Chance from Age
• Age is tracked by individual, and is
incremented every generation
• Two equations explored for SAGE
• Equation 1 (ABPS-AutoEA1): linear
decrease
SAGE = 1 - RA ( AGE)
Rate of decrease from age
Survival Chance from Age (cont’d)
• Equation 2 (ABPS-AutoEA2): more
dynamic
Number of
individuals in the
same age group
SAGE
N AG AGE
=12P
2G
Population size
Number of generations
the EA will run
Survival Chance from Age (cont’d)
N AG - AGE
=
• Effects of SAGE 1
2P
2G
– More individuals of the same age will
decrease their survival chance
NAG
Si
– Age will decrease survival chance relative to
the maximum age (G)
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
Performance Results - TSP size 20
Average over 30 runs
ABPS-AutoEA1 - SAGE =1- RA (AGE)
ABPS-AutoEA2 - SAGE =1-
N AG AGE
2P
2G
Performance Results - TSP size 40
Average over 30 runs
ABPS-AutoEA1 - SAGE =1- RA (AGE)
ABPS-AutoEA2 - SAGE =1-
N AG AGE
2P
2G
Initial Population Size Effect
3 different runs
Tracking Population Size and
Average Age
Same single
run
Equation with Fitness Scaling
• Attempt to fix the lack of selection
pressure from fitness
• New Main Equation
Fitness of i
Fi
Si =
SAGE
FB
Fitness Scaling
Best Fitness
Fi - FW
Si =
SAGE
FB - FW
Worst Fitness
Initial Performance Analysis from
Fitness Scaling Equation
Average over 30 runs
using

SAGE =1-
N AG AGE
2P
2G
Initial Performance Analysis from
Fitness Scaling Equation (cont’d)
• Elitism improved performance slightly
• Roulette wheel (fitness proportional) parent
selection improved performance on a larger
TSPs but performed worse on smaller TSPs
• Independence from initial population size was
maintained
• Adjustment of population size during the run
was improved
Future Work
• Further exploration of fitness
scaling methods
• Test on additional problems
• Compare to other dynamic
population sizing schemes
• Implement age-based offspring
sizing
Questions?