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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
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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: -.047 Standard Deviation: .027
7-Dimensional Spherical Test Function Experimental Average Fitness: -633.2 Standard Deviation: 705.76 Standard Average Fitness: -.648 Standard Deviation: .244
10-Dimensional Spherical Test Function Experimental Average Fitness: -3946 Standard Deviation: 6604.96 Standard Average Fitness: -2.8 Standard 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?
