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evolution

evolution. the only process with a track-record of developing intelligent minds the process... “blind” & long timescale but... robust, adaptable, minimal requirements. natural evol – timescale (1). natural evol – timescale (1). natural evolution. fundamentals genomes & phenomes

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evolution

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  1. evolution the only process with a track-record of developing intelligent minds the process... • “blind” & long timescale but...robust, adaptable, minimal requirements

  2. natural evol – timescale (1)

  3. natural evol – timescale (1)

  4. natural evolution fundamentals • genomes & phenomes • parental selection • mutation & recombination

  5. humans from genes • DNA (4 bases ATCG, a digital code) • 23 x 2 chromosomes (volumes of design) • DNA → amino acid → protein → organ → human body • body construction complex,many gene interactions

  6. artificial evolution • genomes & phenomes (pipe bends, aerofoils & software) • parent selection • mutation & recombination • timescales • a “blind & selfish” process

  7. AI evolution • Alife • population evolution • other genetic algorithms

  8. population evolution an example

  9. a sample world

  10. genomes & phenomes • binary genome • rule based phenomes • rules specified using sensors & effectors

  11. converting ‘genes’ to rules

  12. crossover & mutation

  13. encoded rules (previous example)

  14. genome modification • cross-over & mutation rate parameters • consider max/minimal mutation • consider max/minimal cross-over • evolving parameter values

  15. the process Create initial population Convert individuals to rulesets Test & score rulesets Select parents & generate new population

  16. parent selection (basic example) if random( 100 ) => 76 76 > 54 and 76 <= 88, 76 is in Sally’s sum-F bucket so Sally chosen

  17. selection strategies • elitism • weak & strong selection • population toroids • clusters • wandering mates • puberty & old-age

  18. toriod

  19. toriod clusters

  20. toriod - wandering

  21. testing • single tests & sets of tests • static tests & progressive tests • evolving tests • solution & test - predator / prey

  22. tutorial example

  23. details • 60 bit genome • 20 * 3 bit N/S/W/E moves (+ 4 n/a moves) • tester judges progression to SE corner typically sensitive to... • population size • selection strategy • etc

  24. in lisp... (defun evolve () (report-header) (create-initial-population) (dotimes (n *no-generations*) (generate-phenomes) (tester) (report n) (when (last-generation...) ...) (setf *population* (breeder)) ))

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