Evolution in a visual virtual world

1. Pacman as an evolving organism Evolution in a visual virtual world

2. The simulated world • A 2D world of moving “animals” ala Pacman eating “plants”. • Graphically represented • Different zoom levels • Run status • Extra info • Torus topology (no boundaries)

3. Run options • Size of the world • Initial population • Max attainable energy per grown plant • Value of growing plants(vs grown) • One metabolic parameter • Graphic, background or analysis mode • Loading of previous run • Switching on or off evolution (mutation)

4. Plants • 2 variants • Growing – dark green, will turn into a grown plant after a given time • Grown – green, can seed growing plants into unoccupied neighbor areas. • Eaten by the “animals”, who can focus their digestion on either grown or growing plants

5. Animals • Moves about in the world. • Eat plants • Can multiply • Has a number of characteristics attached to them • Phenotype • Fixed status (ID, birth time, death time, parent) • Dynamic states: energy, location, direction of movement, activity, age • Mutations directly on the phenotype (genotype=phenotype). Almost no pleiotropy (Diagonal G matrix)

6. Phenotype • Physiological parameters • Speed • Turning speed • Coloration (neutral) • Size/mass • Reproduction parameters • Energy level for reproduction (*mass) • Mutation rate • Metabolic parameters • Specialization for eating grown or growing plants. • Mass • Sensory parameter • Sense of touch • Behavioral parameters • Probability of going straight, turning left, turning right or sitting down and trying to eat • Probability of eating growing and grown plants (conditioned on touch) • Probability of using memory to start in the same direction as one stopped.

7. Energy consumption Energy is consumed by • Creature maintenance (proportional to mass) • Movement • Proportional to mass*speed2 • Starting after having stopped costs more than just maintaining a speed. • Reproducing • Parent looses energy proportional to its mass, then splits the energy in half between it and its child. Energy0 means death.

8. Energy gain Energy is gained by eating plants (either stopping randomly and trying to eat, or by sensing a plant and deciding to stop). Digestion is divided into that for grown plants, Do, growing plants, Dg, and unused digestive capabilities (Du=1-Do-Dg). • Eating grown plants yields energy Eo=Dob-constant (b is a metabolic run parameter). • Eating grown plants yields energy Eg=Dgb-constant.

9. Lession1 – evolution works • Phenotypes for which I know in which direction they should go, actually do go that way. Sense of touch as a function of time Unused digestion (x-axis) as a function of time. Coloration indicates probability of eating a growing plant, if you can feel it.

10. Lession 2 – Evolution doesn’t just happen to the phenotypes for which we have a clear expectation r The energy limit for reproduction has increases as a function of time, here.

11. Lession 3 - Evolution can save a maladapted species from extinction Size of population Without evolution (no variation for selection to act on): With evolution: (Simulation run for 8 times the time as for no evolution, and still no extinction for two simulations) Extinction Size of population Digest grown phenotype

12. Lession4 – Sometimes evolution will go in surprising directions: Looks like specialization on digesting grown plants is preferred: But then the animals “change their minds”:

13. Lession5 – Randomness matters (a little): If we start with the same run conditions and the same animals, we don’t get exactly the same evolutionary trajectory. Digest grown, two runs Number of animals, two runs time

14. Lession6 – It’s not just the mean that’s changing • While we may see evolution in the changing mean, the variance may also be changing:

15. Lession 7 – Speciation is hard to arrange The division into growing and grown plants was an easy extension to allow for speciation. Speciation only seen for one run with extremely fine-tuned digestion setup and a huge world.

16. Lession 8 – Extinction is a possibility, but not a certainty on reasonable time scales With contingency and (pseudo)randomness, there’s always a chance that the population will dwindle and disappear. #animals Eventually all such populations will go extinct. However, that doesn’t need to happen in a ludicrously long time. time (a huge amount of it) Histogram of number of animals for non-evolving population given a little more energy per plant.

17. Lession 9 – Sometimes you can get too much of a good thing If I pump too much energy into each plant (or increase the hunting efficiency) then extinction by over-grazing becomes a near certainty.

18. Lession 10 – Vulnerability to extinction goes down with increasing “world” size Same run parameters, but world area x100:

19. Lession 11 – Predator-prey cycles evolve Large world, mutations switched on: It might look like the population size is getting more noisy with time… #animals time But it’s really predator-prey cycles. With evolution switched off, the population would crash, or with slightly higher energy levels, stabilize.

20. A look at Lotka-Volterra models for predator-prey relationships • Lotka-Volterra: • With low k and c, the deterministic (without noise) system will stabilize to a single value. • With stochasticity, the process will nevertheless reach a stable distribution. Advantage of catching each prey Catchment efficiency #animals time

21. Lotka-Volterra models – gradually increasing hunting efficiency #animals In the start, the system remains deterministically stable, but quickly becomes stochastically cyclic. After a while, cycles even without noise. Cycles become more and more extreme. Population under considerable extinction risk. time #animals #animals time time

22. Lession 12 – Adaptive evolution has no foresight – Darwinian extinction! It’s entirely possible for a species to adapt itself to death! * • For a particular run option, a non-evolving system seem to work stably. • The corresponding evolving system develops gradually more extreme predator-prey-cycles until the plants are wiped out. The animals then starve to death. #animals time (a huge amount of it) #animals, #plants time * Colleen Webb (2005): A Complete Classification of Darwinian Extinction in Ecological Interactions, The American Naturalist 161(2), DOI: 10.1086/345858

23. Lession 13 – Effects of Darwinian extinction on choice of starting conditions Poor starting phenotype (low sense of touch/digestive capabilities) means either immediate extinction due to inefficiency (low plant energy) or later Darwinian extinction (high plant energy). Can’t start off with having too much evolutionary potential, that is. Darwinian extinction seems somewhat softened by world size.

24. Lession 14 – There is randomness in Darwinian extinctions also Did 15 runs with under the same run conditions. A histogram of time to system crash shows stochasticity. Not an exponential distribution, though. Phenotype at end also varies a little (about 7% for important phenotypic characters). For some runs, the phenotype was almost stable for a long time before the crash. => You don’t get a specific phenotype, then die. You get near a specific phenotype and then come under increasing risk. Survival curve suggesting increasing hazard (risk). 3 of the 15 runs ended with a green world. tend

25. Lession 15: Changing hazard (survival analysis) Suggests constant hazard for non-evolving organisms. Decreasing hazard for young to moderately old. Increasing hazard for really old organisms? (Surviving until evolution catches up to them?)

26. Sourcecode • Sourcecodecan be found in my librarynamedhydrasub: http://folk.uio.no/trondr/hydrasub • For Linux (64 bit RedHat) users at CEES, theexectuable is found at ~trondr/prog/evol12 • Presentation: http://folk.uio.no/trondr/pacman_evol/