1 / 12

Final report

Valery Frolov. Final report. Content of the talk. The algorithm Fitness function Crossover Mutation Elite individuals Reverse mutations Some statistics Run examples. The algorithm. The most standard evolution algorithm While (condition isn’t met) Select fittest individuals

shelley
Download Presentation

Final report

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Valery Frolov Final report

  2. Content of the talk • The algorithm • Fitness function • Crossover • Mutation • Elite individuals • Reverse mutations • Some statistics • Run examples

  3. The algorithm • The most standard evolution algorithm • While (condition isn’t met) • Select fittest individuals • Crossover • Mutation • Update population • The condition • Simulation time

  4. Fitness function • The parameters we have • Best laptime • Top speed • Distance raced • Damage done • The function is :

  5. Crossover function • Ideal crossover function forour problem • Learns the relation between different parameters • Performs the crossover accordingly • My crossover • Interleaves two parents with equal probability • Creates one sibling

  6. Mutation function • Each one of 22 parameters is mutated with a probability of 0.1 • Therefore, each individual is mutated with a high probability

  7. Updating the population • Each new population consists of • Elite individuals from previous population • Individuals created by crossover • Several random individuals

  8. Elite individuals • Each population consists of K individuals • Best M individuals are copied to the next population. • M is 1/3 of population size • Crossovers are done with at least one elite parent

  9. Reversed mutations • Problem : • Mutations can make things worse • Elite individual that survived many generation can be removed because of one bad mutation • Our solution : • Reverse previous mutation if condition is satisfied

  10. Size of population is 16 • Time per each individual is 4450 ticks • Individuals get bonus points for surviving • Individuals get penalty for non-completed laps

  11. Some statistics • In most cases starting population consists of pair of “good” and many “bad” individuals • At least half of final generation are “very good” individuals

  12. Run examples

More Related