genders and eas using gestation periods to control population dynamics cameron johnson
Post on 22-Dec-2015
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Genders and EAs
Using Gestation Periods to Control Population Dynamics
Cameron Johnson
Motivation & Justification• Inspiration from biology
• “Black Box” for EAs
Why Genders?
• Panmictic mating produces results
• Meta-EAs and self-adaptive, self-regulating EAs
Methods• Algorithm basics
– Fitness used as mate-selection algorithm– Gestation period
• Population size-control• Restriction on reproductive speed
– Child Support• Balance between own survival and offspring survival
• Behavioral Genes– Male and female child support %– Male and female faithfulness (expressed as %)– Male and female mutation rates (expressed as %)– Sex allele – male or female?
Mate Fitness• Females are simply ranked by normalized fitness
– The fittest female chooses her mate first
• Males’ fitness is modified from its base to create an “attractiveness”
Mate Selection & Child Support
• Females choose based on promises
• Male promise reduced for each promise made
• Male and female real fitnesses reduced by child support
Factors to Keep Track of
• Is the individual alive?
• Who are his parents (father & mother)?
• Is the individual pregnant?
• With whom did the individual last mate?
• How many children does the individual have?
4-Dimensional Spherical Test Function
Experimental Average: -4.5 Standard Deviation: 4.57
Standard Average: -.047Standard Deviation: .027
7-Dimensional Spherical Test Function
Experimental Average Fitness: -633.2Standard Deviation: 705.76
Standard Average Fitness: -.648Standard Deviation: .244
10-Dimensional Spherical Test Function
Experimental Average Fitness: -3946Standard Deviation: 6604.96
Standard Average Fitness: -2.8Standard Deviation: .64
Conclusions
• Performance is disappointing– Accuracy cannot keep up with standard algorithm
even on a simple problem
• Population cannot always recover from collapse due to premature convergence– Likely due to loss of genetic diversity
• Population dynamics are self-adaptive, so promise is shown, but not yet achieved
Future Work
• Rebuilding with a more efficient implementation for quicker data-taking
• Experiment with different mate-selection parameters for genetic diversity
• Try hard-set and heuristic-adjusted mutation rates
• Generally, continued analysis of causes for sub-optimal performance
Questions?
• “A man pushes a car up to a hotel and tells the owner he is bankrupt. Why?”
• “A man lies dead next to the rock that killed him. Why is his underwear visible?”
• “Fred and Gertrude lie dead amidst a puddle of water. Shards of broken glass are scattered everywhere. What killed them?”
• “Who is the greater inventor: Darwin for evolution, or Al Gore for the Internet?”
Answers!
• Now that would be telling, wouldn’t it?
4 Dimensions, First Run
0 0.5 1 1.5 2 2.5
x 104
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1000
2000
3000
4000Population vs. # of Fitness Evaluations
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
best fitness
average fitness
4 Dimensions, Second Run
4 Dimensions, Third Run
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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4 Dimensions, Fourth Run
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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4 Dimensions, Fifth Run
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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4 Fitness fs. # of Fitness Evaluations
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4 Dimensions, Sixth Run
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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4 Dimensions, Seventh Run
4 Dimensions, Eighth Run
4 Dimensions, Ninth Run
4 Dimensions, Tenth Run
0 0.5 1 1.5 2 2.5
x 104
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4 Fitness fs. # of Fitness Evaluations
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7 Dimensions, First Run
7 Dimensions, 3rd and 4th Runs
0 0.5 1 1.5 2 2.5
x 104
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0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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average fitness
7 Dimensions, 5th and 6th Runs
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4 Fitness fs. # of Fitness Evaluations
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average fitness
7 Dimensions, 7th and 8th Runs
7 Dimensions, 9th and 10th Runs
0 0.5 1 1.5 2 2.5
x 104
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1000Population vs. # of Fitness Evaluations
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
best fitness
average fitness
0 0.5 1 1.5 2 2.5
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0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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average fitness
10 Dimensions, 1st and 2nd Runs
10 Dimensions, 3rd and 4th Runs
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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4 Fitness fs. # of Fitness Evaluations
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average fitness
10 Dimensions, 5th and 6th Runs
10 Dimensions, 7th and 8th Runs
0 0.5 1 1.5 2 2.5
x 104
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1000Population vs. # of Fitness Evaluations
0 0.5 1 1.5 2 2.5
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4 Fitness fs. # of Fitness Evaluations
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average fitness
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
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4 Fitness fs. # of Fitness Evaluations
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10 Dimensions, 9th and 10th Runs
0 0.5 1 1.5 2 2.5
x 104
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0 0.5 1 1.5 2 2.5
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-6
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4 Fitness fs. # of Fitness Evaluations
best fitness
average fitness