Part 1 - Natural Genetics

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Part 1 - Natural Genetics

• Ben Paechter
• with thanks to the EvoNet Training Committee and
  its Flying Circus

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Natural Genetics

• The information required to build a living
  organism is coded in the DNA and other genetic
  material found in the cells of that organism
• Within a species, most of the genetic material is
  the same
• Small changes in the genetic material give rise
  to small changes in the organism
• E.g height, hair colour

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DNA and Genes

• DNA is a large molecule made up of fragments.
  There are several fragment types, each one acting
  like a letter in a long coded message
• -A-B-A-D-C-B-B-C-C-A-D-B-C-C-A-
• Certain groups of letters are meaningful together
  - a bit like words.
• These groups are called genes
• The DNA is made up of genes and rubbish

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Example Human Reproduction

• Human DNA is organised into chromosomes
• Most human cells contains 23 pairs of chromosomes
  which together define the physical attributes of
  the person

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Reproductive Cells

• Sperm and egg cells contain 23 individual
  chromosomes rather than 23 pairs
• Reproductive cells are formed by one cell
  splitting into two
• During this process the pairs of chromosome
  undergo an operation called crossover

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Crossover

• During crossover the chromosome pairs link up
  and swap parts of themselves
• Before After
• After crossover one of each pair goes into each
  cell

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Fertilisation
Sperm cell from Father
Egg cell from Mother
New person cell
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Mutation

• Occasionally some of the genetic material changes
  very slightly during this process
• This means that the child might have genetic
  material information not inherited from either
  parent
• This is most likely to be catastrophic

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Theory of Evolution

• From time to time, reproduction, crossover and
  mutation produce new genetic material or new
  combinations of genes
• Usually this reduces the organisms ability to
  survive and so reproduce
• Occasionally the new genetic material increases
  the organisms ability survive and so reproduce
• If it allows the organism to reproduce more then
  this leads to more and more organisms have the
  new improved genetic make-up
• Good sets of genes get reproduced more
• Bad sets of genes get reproduce less

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Theory of Evolution (2)

• The organisms as a whole get better and better at
  surviving in their environment
• Evolutionists claim that all the species of
  plants and animals have been produced by this
  slow changing of genetic material - with
  organisms becoming better and better at surviving
  in their niche, and new organisms evolving to
  fill any vacant niche
• They agree that evolution requires reproduction,
  selection and mutation
• Some say evolution also requires crossover

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Evolution as Search

• We can think of evolution as a search through the
  enormous genetic parameter space for the genetic
  make-up that best allows an organism to reproduce
  in its changing environment
• Since it seems pretty good at doing this job, we
  can borrow ideas from nature to help us solve
  problems that have an equally large search spaces
  or similarly changing environment

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Dr. Eicks Transparencies Genetics and What EC
AlgorithmDesigners can learn from it
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More Genetics Diploidy and Dominance

• Diploidy Most chromosomes in biological systems
  are double-stranded(diploid) and not
  single-standed(haploid) carrying pairs of
  chromosomes each containing information for the
  same function.
• The primary mechanism to select which genotypical
  information will be expressed in the phenotype is
  dominance
• AbCDe aBCde ????ABCDe
• Diploidy provides a mechanism for remembering
  alleles and allel combinations that were
  previously useful dominance provides a mechanism
  to shield those remembered alleles from harmful
  selection in a current hostile environment
  (increasing implicitly the richness of the genes
  expressed in the current population by providing
  a shield against overselection).
• Dominance relationships frequently adapt in
  biological systems when the need arises.
• Hollstien(1971) simulated dominance using a three
  letter instead of a binary alphabet consisting
  of dominant 1, non-dominant 1, and 0 with
• 1dom gt 0 and 1rec lt 0.

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Dominance and Diploidy (Continued)

• Other research represents the dominance
  information separately from the gene and lets it
  undergo evolution --- a kind of co-evolution
  approach.
• In the late 70s, Smith and Goldberg explored the
  use of redundancy for the normal knapsack problem
  with dynamic weight changes
• Holsteins triadic scheme showed improvement over
  a static dominance scheme.
• it turned out that the diploid approach coped
  better with ascillations in the weight function.
• decreases the probability that desired schemas
  are lost forever.
• In summary, there seems to be some evidence that
  exploiting diploidy can be beneficiary for GAs in
  dynamically changing environments, especially if
  scenarios encountered in the past have a tendency
  to reoccur in the future on the other hand,
  diploidy is quite expensive, and not too much
  research has been performed in the last 15 years
  that explores its use for GAs.

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What can GA-designer learn from plant genetics
and horticulture?

• polyploidy and dominance
• gametogenesis is used as the crossover operator
• use of selfing
• unusual ways to prevent self fertilization
• use of intercrossing (create cartesian products
  of good initial solutions)
• preference for heterozygous sources and rich gene
  pools
• plant breeders employ complex search strategies
  to breed the best possible plant (such as
  recurrent selection, which will be the topic of
  this talk).
• mutation not very important, because it is hard
  to control large population sizes are difficult
  to handle because of pragmatic reasons.

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Polyploidy

• Polyploidy using two are more complete sets of
  chromosomes the
• phenotype of an organism is determined through
  dominance of alleles.
• Advantages adaptation to changing environments,
  memorize alleles that worked successfully in
  the past, richer gene pool.
• Previous Research on Polyploidy two major
  approaches to simulate polyploidy in GAs
• using an extra chromosome to represent dominance
  information Brindel, this talk
• extending the alphabet to distinguishes between
  dominant and recessive elements Holstein,
  SmithGoldberg, NgWong

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Features of our Approach

• uses at least 2 sets of chromosomes
• uses a dominance vector as a tie breaker
• uses a crossover control vector to restrict
  possible crossover points
• dominance vectors and crossover control vectors
  take part of the evolution
• gametogenesis is used as the crossover operator

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3. Experiments

• Benchmarks
• Knapsack problem with dynamically changing weight
  constraints
• Schwefel function
• Evaluation is performed with respect to the
  following measure
• M2? (Ti-Xi)2/G
• where Ti is the true optimimum for
  generation i and Xi is the best solution found in
  generation i, and G is the number of generations.

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4. Summary

• proposed an approach to support polyploidy that
  uses dominance vectors
• demonstrated the benefits of the approach in
  oscillating environments which cycle among
  several different states.
• crossover control vectors are employed to provide
  linkage between the dominance vector and the
  chromosomes themselves.
• approach facilitates maintaining diversity in
  relatively small populations
• our experiments at least partially explain why
  diploidy and polyploidy exist in biological
  systems.

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Literature

• Ben S. Hadad and Christoph F. Eick Using
  Recurrent Selection to Improve GA-performance,
  ISMIS, Charlotte, October 1997.
• Ben S. Hadad and Christoph F. Eick Supporting
  Polyploidy in Genetic Algorithms Using Dominance
  Vectors, EP97, Indianapolis, April 1997.
• Ben S. Hadad Extending Genetic Algorithms Using
  Ideas Borrowed from Plant Genetics and
  Horticulture, Masters Thesis, University of
  Houston, December 1996.

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Inversion and Other Reordering Operators

• Reordering operators change the position/location
  of genes in a chromosome, but do not change the
  composition of the chromosome
• consequently, reordering operators do not
  directly affect the fitness.
• however, crossover is effected namely, the
  defining length of a schema is changed by
  applying reordering operators, which increases or
  decreases the probability that instances of a
  particular schema reoccur in the future.
• reordering causes that genes are nolonger lined
  up corrrectly, which, in many applications,
  causes problems with the crossover operator
• necessary genes might be missing non-complete
  gene combinations can occur.
• duplicated genes can occur, wbich is usually not
  desirable.
• The most popular reordering operators are
  inversion and swapping
• 1 2 3 4 5 6 7 8 inversion 12376548
  swap 12375648
• Empirical evidence seem to indicate that at least
  in some applications reordering operators are
  useful secondary operator, whose employment
  induces slight improvements in the overall
  performance.

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Niche and Speciation

• We can view a niche as an organisms job or role
  in an environment, and we can think of a species
  as a class of organisms with common
  characteristics.
• Niche Methods in Genetic Search
• crowding (DeJong(1975)) and sharing functions
  (Goldberg(1987)).
• external schemes (Perry(1984)) which are
  similarity templates that define species
  membership that have be provided by the
  GA-developer.
• Mating restrictions in genetic search
• line breading (breed the champion repeatedly with
  others)
• Hollsteins inbreeding with intermittent
  crossbreeding (close individuals still bread as
  long as their family average fitness continues to
  improve otherwise, crossbreeding between
  different families is used).
• Booker introduces mating templates that are mate
  selection mechamisms that become part of the
  individual (which themselves undergo evolution)
  and proposes different mating rules
• bidirectional match
• unidirectional match
• best partial matches
• disallow breeding of simimlar indiduals (e.g.
  incest)

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Example of a Booker Mating Template

• Assume we have chromosomes over alphabet A with
  chromosome length n, and let Aunion(A,).
• Extend chromosomes tripling their length to
• inda1...anb1...bnc1...cn with ai?A, bi and
  ci?A (i1,n) with the meaning
• ind is allowed to mate with ind if
  ind?Schema(b1...bn ) or ind?Schema(c1...cn ).
• Example Let n4 and A be the binary alphabet
• ind10010 0000 1111
• ind20000 1 0111
• ind30111 001 1111
• Bidirectional match requests that a must want b
  and b must want a, whereas in unidirectional
  match it is sufficient that one partner wants the
  other.
• Many other matching schemes are possible e.g.
  more complicated ones that operate on scores and
  thresholds.

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Artificial Mating Tags

• the problem with Bookers approach is that mating
  templates have the same length as the chromosomes
  themselves, producing a significant overhead. To
  reduce this overhead Holland proposed to use a
  three-part strings consisting of
• a short mating template(used to test suitability
  of other mates)
• a short mating tag(used by others to match,
  characterizes the string)
• the functional substring
• Example 101010111111000011
• 01100011111110001
• mating tags effect the compatibility with other
  strings, but do not effect the fitness.
• usually, the three-part string is evolved.
• Hollands scheme of using artificial mating tags
  can also be used to define mating niches
  abstractly, similar to Perrys external schema
  approach, by freezing particular positions in
  templates and tags. For example, mating can
  easily restricted to particular subsets of the
  population. Mating tags can also be used to
  simulate distributed GAs.