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Understanding Evolution: The Process of Developing Intelligent Minds

This article explores the robust and adaptable nature of evolution, emphasizing its role in the development of intelligent minds. It delves into natural evolution fundamentals, including genomes, phenomes, mutation, recombination, and parental selection, highlighting how DNA and proteins construct the human body. Furthermore, it addresses artificial evolution and genetic algorithms, detailing how population evolution can lead to adaptive solutions. Through comprehensive examples and methodologies, this work illustrates the processes of selection, mutation, and testing, providing insights into both natural and artificial evolutionary strategies.

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Understanding Evolution: The Process of Developing Intelligent Minds

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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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