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)

4000 MYA self-replicating RNA

3500 MYA bacteria & proto-photosynthesis

3000 MYA cyanobacteria “pollute” atmosphere with oxygen

1200 MYA sexual reproduction arises

1000 MYA multi-cellular organisms

505 MYA vertebrates

475-450 MYA plants & arthropods on land

300ish MYA insects diversify, land-based reptiles, ferns

150 MYA proto-birds

125 MYA small modern mammal

110 MYA dinosaurs

natural evol – timescale (1)

65 MYA mass extinction no.5 (dinosaur extinction)

primate order starts

30 & 15 MYA primates diverge

700 KYA common ancestor humans & Neanderthals

150 KYA mitochondrial eve (90-200kya)

100 KYA modern humans

50 KYA human expansion

35 KYA symbolism in humans

3 KYA iron tools

AD 0001 human population 150 million

AD 1853 human population 1 billion

AD 2010 human population exceeds 7 billion

natural evolution

fundamentals

• genomes & phenomes

• parental selection

• mutation & recombination

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

artificial evolution

• genomes & phenomes(pipe bends, aerofoils & software)

• parent selection

• mutation & recombination

• timescales

• a “blind & selfish” process

AI evolution

• Alife

• population evolution

• other genetic algorithms

population evolution

an example

a sample world

genomes & phenomes

• binary genome

• rule based phenomes

• rules specified using sensors & effectors

Sensors & Effectors

Sensors Effectorstarget-distancetarget-directionobstacle-distanceobstacle-direction

collect-targetturn-rightturn-leftmove-forward

A Sample Rule

IF target-direction = left and obstacle-direction left THEN turn-left

converting ‘genes’ to rules01000011 IF target-direction = forward THEN move-forward

00 target-distance01 target-direction10 obstacle-distance11 obstacle-direction

00 =01 <10 >11

0 1 0 0 0 0 1 1

00 forward01 right10 left11 backward

00 collect-target01 turn-right10 turn-left11 move-forward

crossover & mutation

Parent 1 genotype: 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 0

Parent 2 genotype: 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 1

Mutation profi le: - - - - - - - - - 1 - - - - - -

Offspring genotype: 0 1 0 0 1 0 0 0 1 1 1 1 1 0 1 0

Offspring rule

IF target-direction = left and obstacle-direction left THEN turn-left

encoded rules (previous example)

Parent 1

IF target-direction = forward or target-distance left THEN turn-left

Parent 2

IF obstacle-direction < left and obstacle-distance < right THEN move-forward

Offspring

IF target-direction = left and obstacle-direction left THEN turn-left

genome modification

• cross-over & mutation rate parameters– consider max/minimal mutation– consider max/minimal cross-over

• evolving parameter values

the process

Create initial population

Convert individuals to rulesets

Test & score rulesets

Select parents&

generate new population

parent selection (basic example)

ID F sum-F

fred 20 20 (20+0)

sue 15 35 (15+20)

bill 19 54 (19+35)

sally 34 88 (34+54)

nancy 3 91 (3+88)

steve 9 100 (9+91)

...etc...

if random( 100 ) => 7676 > 54 and 76 <= 88, 76 is in Sally’s sum-F bucket so Sally chosen

selection strategies

• elitism

• weak & strong selection

• population toroids– clusters– wandering mates

• puberty & old-age

toriod

toriod clusters

X

toriod - wandering

** * **

X * *

testing

• single tests & sets of tests

• static tests & progressive tests

• evolving tests

• solution & test - predator / prey

• predator/prey with toriods, etc

peformance profiles

average performance

variance

graphs borrowed from Peter Andras, Newcastle University

rule interpretation1. Rules: {ante => conseq} use First Firing

2. Rules: {ante => conseq / weight} use max weightNB: may also use certainty factors or fuzzy logic

3. Rules : {ante => effector / weight}use effector with max accumulated weightNB: may also use certainty factors or fuzzy logic

4. Rule networks…{N: ante => conseq || on-fail-goto}

5. {N: ante => conseq, weight, threshold => on-success-goto || on-fail-goto}

6. activity nets{ f( g(a,b), h(c,d)) / Г }…etc

summary #1

you need well constructed...

• problem encoding (world, sensors, effectors, fn.s, etc)NB: if you don't know what is/isn't important then over supply

• phenome construction (avoid ambiguity)

• evolutionary operators (xover, mutate...)

• test strategy & fitness evaluation (be prepared to "tune" fitness function to improve performance)

• mate selection

summary #2

elitism, over-strong selection => quick elimination of "weaker" performers. May also remove useful but "sleeping" genes from population.

=> early convergence

summary #3

narrow spread of performances=> convergence (ie: little genetic variation)

large spread of the performancescheck: problem encoding, genetic operators & selection strategy

ditto for too fast/slow increase in average performance

try questions from page 24 of...

http://www.staff.ncl.ac.uk/peter.andras/evocompappl.ppt

tutorial example

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

in lisp...

(defun evolve ()

(report-header)

(create-initial-population)

(dotimes (n *no-generations*)

(generate-phenomes)

(tester)

(report n)

(when (last-generation...) ...)

(setf *population* (breeder))

))