Age-Based Population Dynamics in Evolutionary Algorithms
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Transcript of Age-Based Population Dynamics in Evolutionary Algorithms
Age-Based Population Age-Based Population Dynamics in Dynamics in Evolutionary Evolutionary AlgorithmsAlgorithms
Lisa GuntlyLisa 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
SiFiFBSAGE
Fitness of i
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
SAGE1 RA (AGE)Rate of decrease from age
Survival Chance from Age (cont’d)
• Equation 2 (ABPS-AutoEA2): more dynamic
SAGE1 NAG2P
AGE2G
Number of individuals in the same age group
Population size Number of generations the EA will run
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
SAGE 1 NAG2P
AGE2G
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 -
ABPS-AutoEA2 -
SAGE 1 RA (AGE)
SAGE 1 NAG2P
AGE2G
Performance Results - TSP size 40
Average over 30 runs
ABPS-AutoEA1 -
ABPS-AutoEA2 -
SAGE 1 RA (AGE)
SAGE 1 NAG2P
AGE2G
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
SiFi
FB FWFWSAGESi
FiFBSAGE
Fitness of i
Best FitnessWorst Fitness
Fitness Scaling
Initial Performance Analysis from Fitness Scaling Equation
Average over 30 runs
SAGE 1 NAG2P
AGE2G
using
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?Questions?