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: Standard Deviation:.027

7-Dimensional Spherical Test Function Experimental Average Fitness: Standard Deviation: Standard Average Fitness: Standard Deviation:.244

10-Dimensional Spherical Test Function Experimental Average Fitness: Standard Deviation: 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?

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