Spring 2006 Artificial Intelligence COSC40503 week 13

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Spring 2006 Artificial Intelligence
COSC 40503week 13

• Antonio Sanchez
• Texas Christian University

References www.cs.vu.nl/gusz/ecbook/slides/Geneti
c_Algorithms.ppt A.E. Eiben and J.E. Smith,
Introduction to Evolutionary Computing Genetic
Algorithms web.umr.edu/ercal/387/slides/GATutoria
l.ppt igda-phx.org/resources/20031121_ga.ppt paras
ol.tamu.edu/amato/Courses/ 689-608/presentations/
jkp2866/gep-jack.ppt www.niss.org/affiliates/prote
omics200303/ presentations20030306/0420Jennifer.p
pt http//www.eece.unm.edu/akbazar/
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A formal presentation of GAsThe Gene is by far
the most sophisticated program around.- Bill
Gates, Business Week, June 27, 1994

• When to use them
• Alternate solutions are too slow or overly
  complicated
• Need an exploratory tool to examine new
  approaches
• Problem is similar to one that has already been
  successfully solved by using a GA
• A quasioptimal solution is allowed
• When no to use them
• If you need the optimal solution
• If you have the Analytical solution or it can be
  easily solved (one variable functions)
• If the solution is a convex function
• If the space is discrete and enumerable

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Definitions

• Genetic From Greek genno ?e??? give birth)
• Algorithm From Farsi, belonging to the a
  prominent Muslim mathematician Al Khowarizmi
  (790-840). Advocates of the new math system were
  called algorists as opposed to the abacists who
  continued to use the abacus inherited from the
  Romans. The first use of the word Algorithm is
  from Liebnitz in the late 1600 referring to a
  method of solving problems my means of sequence
  of procedures that loops and branches depending
  on what's coming up for them thereby optimizing
  their chances of having a productive experience.
• .

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Definitions

• Allele Range of values that the gene can take
  once it is decoded
• Chromosome A string of binary genes
• Gene A binary string that codifies an
  element of
• the Phenotypical Function
• Generation Discrete moment in time during the
  simulation
• Genotype Is the binary codification of the
  chromosome that has the genes (g0,g1,g2,gn)
  (000,1111,01010,001)
• Locus Location of the gene in the Chromosome
• Peformance Evaluation Method used by the
  environment to determine the fitness of a
  chromosome
• Phenotype Is the analytical function to evaluate
• ef ?ciFpi 15D?K 5D??G 20D?t
  2D??l 3D??s
• Population Collection of Chromosomes in a given
  generation
• Schemata A string of binary digits that uses
  to represent either 0 or 1 11
  represents 4 strings 0110, 0111, 1110, 1111

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History

• 1859 Charles Darwin publishes The Origin of
  Species
• 1865 Gregor Mendel publishes paper Experiments
  on Plant Hybridization
• 1905 William Bateson coins the term "genetics" in
  a letter to Adam Sedgwick
• 1910 Thomas Hunt Morgan shows that genes reside
  on chromosomes
• 1953 James Watson and Francis Crick with Rosalind
  Franklin resolved structure DNA as a double helix
  
• ____________ Artificial GAs ___________________
  __
• 1975 John Holland publishes Adaptation in Natural
  and Artificial Systems
• 1989 David Goldberg publishes Genetic Algorithms
  in Search, Optimization and Machine Learning,

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Gas Detailed History

• 1948 Turing proposed the search by means
  evolutionary or genetics
• 1962 Bremermann proposed optimization through
  recombination and evolution
• 1964 Rechenberg introduced the concepts of
  evolution strategies
• 1965 Fogel, Owens and Walsh introduced
  evolutionary programing
• 1975 J. Holland the recognized father of GA
• 1989 D. Goldberg big promoter of the use of GA
  for search and learning
• 1992 Koza introduces genetic programming

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Genetic Operators

• Encoding
• Method of representation of the coefficient in
  a gene
• Mutation
• When a bit inside the chromosome is invested
• Internal positional swap
• When two bit inside the chromosome
• exchanges their position (arity1)
• Reproduction
• When a new population is generated
• Crossover (aka recombination)
• When randomly two chromosomes are chosen bits
• are crossed over generating two new individuals
  (arity 2)
• Substitution
• When the parents of a crossover are eliminated
  from the population
• Decoding
• Process of obtaining of the value for a
  coefficient

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GA general process

• Determine the task to evaluate
• Codify the Chromosome and number of genes
• Define the initial population
• Criteria of selection by the environment
• Random selection of survivors
• Chromosome crossing
• Mutation
• Evolution

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GA operators Selection

• Main idea better individuals get higher chance
• Chances proportional to fitness
• Implementation roulette wheel technique
• Assign to each individual a part of the roulette
  wheel
• Spin the wheel n times to select n individuals

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Crossover OR Mutation?

• Exploration Discovering promising areas in the
  search space, i.e. gaining information on the
  problem
• Exploitation Optimising within a promising area,
  i.e. using knowledge or information
• Crossover is explorative, it makes a big jump to
  an area somewhere in between two parent
• Mutation is exploitative, it creates random small
  diversions, thereby staying near the parent

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Initial Population

• Although the initial population is assumed random
  you may
• consider the following options
• Intelligent Design
• If possible start with some range values for the
  function coefficients to faster convergence.
  However remember that this might work like the
  Crash Cut of Minimax
• A Priori Seed
• Similarly you can start with some given alleles
  or seed values. For example use the values you
  obtain from your own experience.
• Diversity control to avoid Inbreading and Locking
  
• If you use the previous considerations remember
  to add soe diversity either by mutation or in the
  evaluation function to allow to step out of the
  box you have predefined

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Phenotype/Genotype Mapping

• The mapping can be
• Encoded such is the case of classical GA
• Uncoded or direct such is the case of EA or
  Evolutionary Algorithms
• And so the genotype can represent a
• An Integer
• A Real number within a range
• An assign task within an scheduling problem
• The branch of a decision tree (an S expression)
• A case within a enumerable switch

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Chromosomes in time

• To determine which chromosomes are selected in
  next generation may include aspects such as
• Diversity
• Parallelism
• Elitism
• Specialization Niches
• Meta Chromosomes
• Substitution rather than Addition
• Deception
• Always plots
• Best in generation
• Generation as a whole

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On Natural Genetics

• ALL the proteins of the live beings in our planet
  are composed of sequences of 20 amino acid
• The DNA that forms the chromosomes is built by
  four nucleotidos in the shape of double helix
• Purines Adenosina, Guanina
• Pyrimidines Thiamine (or Uracil), Citosina
• The code is TEtranary digiT (TET) A,G,T,C
• Codons are triplets of nucleotides (ATC, GTC,)
• just as bytes related to 8 bits. Yet there
  are
• only 20 amino acids so there is great amount
• of redundancy in the codes.
• Furthermore the DNA contains a
• high percentage of chains are not used
• that help in schemata resolutions.
• The DNA is used in the three living species
• Plants, Animals and Fungi

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Schemata Analysis

• Schemata are similar strings of bits that contain
  either 0,1,. For example
• 0000 strings of 5 bits that begin with a 0
  or a 1
• (10000) , (00000)
• 00 strings of 4 bits that begin with a 0
  or a 1 and end with a 0 or 1
• (0000, 0001, 1000, 1001)
• Thus every population of chromosomes can be
  tipified diverse schemata specifically the
  population is going to have (L1)k schemata
  where k is the cardinality of the language (in
  this case 2) and L is the length of the
  chromosome.
• Every schemata H has some properties such as
• Order OR (H) I number of fixed positions.
• Examples Or (1111) 4, OR (111) 3
• Length dL (H) Greater distance among the fixed
  positions of H
• Examples dL (111) 5-1 4, dL (111) 4-2
  2

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Schemata Analysis

• It is intuitive to notice that the DL of a given
  H plays an important part in the probability of
  destruction or survival of the H through the
  time.
• The longer the string that defines H the greater
  its probability of extinction in the next
  generations
• For this reason the hypothesis GAs is that the
  construction blocks of a genes in a chromosome
  should be small
• The fundamental Theorem of the AG tells us that
  
• Schemata of low order, with reasonable
  adaptation, will have an exponential growth in
  the number of its instances in subsequent
  generations of chromosomes

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Softcomputing

• Softcomputing refers to a set of problem solving
  methods
• used when the required solution
• Permits some degree of imprecision
• It is subject to uncertainty
• Deals with knowledge(beliefs, choices) rather
  than information(truth)
• A quasi optimized solution is acceptable
• Allows ample time to come with the solution
• In this case soft computing such as GAs offers
  solutions with
• Robust results
• Low cost
• Easy tractability

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Sources and References

• Mohammad Akbarzadeh a tutorial on Genetic
  Algorthms URL http//www.eece.unm.edu/akbazar/
• Sanghamitra Bandyopadhyay URL
  http//ranger.uta.edu/bandyopa/demo_uta.ppt
• Tracey Cool URL http//www.msm.cam.ac.uk/phase-
  trans/2002/Harrypresarial/
• URL www.csug.rochester.edu/users/ugrads/tkollar
  / content/csc240/RoboCupLecture2a.ppt
• AE Eiben and JE Smith URL www.cems.uwe.ac.uk/j
  smith/ecbook/ slides/Genetic_Algorithms.ppt
• Zbigniew Michalewicz URL http//www.cs.itu.edu.
  tr/etaner/courses/BE_EvoComp03_04/
• John Siirola URL giskard.cheme.cmu.edu/class/Ge
  neticLecture/ 11-genetic-algorithms.ppt
• Wendy Williams URL http//web.umr.edu/ercal/38
  7/slides/GATutorial.ppt
• Trevor Meyerowitz Claudio Pinello
  URLhttp//www.dac.com/40th/40acceptedpapers.nsf/0
  /713e4a6002949a7f87256dc60058c222/
  FILE/dp19_3.PPT

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Sources and References

• Thais Melo URL http//www.cs.princeton.edu/tm
  elo/pres.ppt
• Gerardo Mendoza and Dan Reich URL
  http//cow.math.temple.edu/cow/cgi-bin/manager
• Jim Cohoon and Kimberly Hanks URL
  http//www.cs.virginia.edu/evans/bio/slides/ga1.p
  pt
• Jennifer Pittman URL http//www.niss.org/affili
  ates/proteomics200303/ presentations20030306/0420
  Jennifer.ppt
• Peter Cowling, Graham Kendall, Limin Han URL
  http//www.cs.nott.ac.uk/lxh/cec2.pdf
• Eyal Allweil and Ami Blonder URL
  http//www.cs.huji.ac.il/course/2002/aisemin/talks
  /week8a.ppt
• Noyan Turkkan URL http//www.umoncton.ca/turk/G
  enetik201.xls
• EfraimTurban Jay E. Aronson URL
  http//mgtclass.mgt.unm.edu/Bose/MGT20539/
• Hitch Hiker URL http//www.cs.bham.ac.uk/Mirror
  s/ftp.de.uu.net/EC/clife/www/Q99_E.htm
• Jim Smith, Agoston Eiben Jano van Hemert
  http//www.evonet.polytechnique.fr/evoweb/resource
  s/flying_circus/slides/