Based on âBuilding Blocks, Cohort Genetic ... Review: GA Crossover ... bits that are near useful building blocks tend to persist by virtue of proximity â not utility.
Cohort Genetic Algorithms Based on “Building Blocks, Cohort Genetic Algorithms, and Hyperplane-Defined Functions” by John H. Holland presented by Jeff Wallace CS 790R
Review: GA Crossover
Parent genomes recombine to form 2 new genomes Crossover point(s) determined at random Parents
Children
Review: GA Mutation
Random gene mutates with (low) probability
Before
After
Review: Selection Roulette-wheel selection determines parents of next generation
p1 P2 P3 p4
Score 60 33 19 7
Norm .504 .277 .160 .059
1 2 3 4
Review: Basic Algorithm choose initial population repeat evaluate each individual's fitness select best-ranking individuals to reproduce mate pairs at random apply crossover operator apply mutation operator until terminating condition
Schema
aka similarity templates describes how strings are similar at specific positions uses wildcards (‘*’) to describe parts of the template that are not relevant e.g. *111* describes {01110, 01111, 11110, 11111} “order” is the number of fixed positions high order is more specific/defining than low order “defining length” is the distance between the first/last fixed positions schemata aka hyperplanes USEFUL schemata are “building blocks”
number of schemata manipulated is much larger than the number of strings explicitly processed (~n3 , n=pop size). aka “implicit parallelism” not good for finding best individual not best approach for highly correlated landscapes good for finding improvements in uncorrelated landscapes
Problem with Basic GA: Hitchhiking
bits that are near useful building blocks tend to persist by virtue of proximity — not utility. hitchhiking loci tend to be underexplored. solution: scale reproduction rate for best string downwards (toward 1.0)
Problem with Hitchhiking Solution: Fractional Offspring e.g. best string = reproduction rate of 1.2
second offspring created with p = 0.2 after 4 generations, expect to find 2 copies of individual (1.24 = 2) variance is large, however. The probability of only one copy is 40%, meaning that useful schemata are lost.
Solution to the Problem of the Solution: Cohort GAs?
designed to allow low scaling of reproduction rates without high variance caused by stochastic approach to fractional offspring idea: fitness determines how long a string has to wait before reproducing
higher-fit strings reproduce more quickly, thus having a higher number of progeny over time.
Cohort GA Implementation
divide population into ordered set of non-overlapping subpopulations (cohorts) Reproduction function cycles through cohorts (in order)
cGA Reproduction (within cohort)
each string produces 2 offspring each string is evaluated and scaled to low reproductive rate (e.g. 1.2). Average reproductive rate = 1.0 calculate doubling time* (DT) at this reproductive rate (e.g. =4 @ 1.2). * at low rates, a linear function may be used place this offspring in the cohort DT steps “ahead” of current cohort.
cGA Tweaks
cohorts will contain different pop sizes. mating produces 4 offspring (2 each parent) if population bounded, strings must be deleted. You want to delete low-fitness strings without emptying distant cohorts preserve diversity by scaling fitness according to commonality. Common alleles reduce fitness. Unique alleles increase it.
Issues
biological analogue? why doesn’t downscaling fitness of common alleles punish strong building blocks? reduces premature convergence, but this isn’t always desirable.
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