Evolution

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Evolution Genetic Algorithms
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Lamarckian Evolution

• Lamarckian Theory
• Based on the concept of use and disuse
• Over a few generations, a given structure or
  organ will increase in size if the creature and
  its parents use that structure often.
• On the other hand, if a structure and organ is in
  disuse it will get smaller and even disappear in
  subsequent generations.

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An Example of Lamarckian Evolution

• A Giraffe has a long neck because its ancestors
  used its neck to reach food.
• Based on Lamarcks theory, the Giraffe of the
  future will have an even longer neck than its
  contemporary relatives.

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Darwinian Evolution

• All animals are constantly changing and evolving
• The primary goal of an animal is to mate and have
  as many offspring as possible
• Concept of natural/sexual selection
• Natural selection, development, and evolution
  requires time

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Darwins Evolution

• A creatures survivability is not the result of
  divine intervention or due to a desire to seek
  perfection.
• It is through the process of natural selection
  that creatures evolve into what they are now.

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Biological Evolution

• Evolution refers to the cumulative changes that
  occur in a population
• Biological evolution is not a random process.
• It is a constantly occurring phenomenon
• Genes are the key components in the process of
  evolution.
• Any physical characteristics acquired during the
  organisms life are not transferred to their

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Biological Evolution And Genetic Algorithms

• Biological Evolution is the inspiration for
  genetic algorithms
• Most of the principles associated with biological
  evolution also apply to genetic algorithms
• Unlike evolution, genetic algorithms will stop
  after a finite number of gnerations

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What Are Genetic Algorithms

• They are essentially search algorithms
• Given a large search space, GAs will evolve to
  the correct solution to a problem over a series
  of generations.
• GAs do not guarantee an optimal solution to a
  problem
• ie. Traveling salesman problem

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What are Genetic Algorithms continued

• Genetic Algorithms are useful at finding
  acceptably good solutions acceptably quickly
• Nevertheless, if an optimized strategy already
  exists for a given problem, it is best to use it
  rather than a GA.

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Components of a Genetic Algorithm

• The population of potential solutions
• A fitness function
• A process for selecting mating pairs and
  introducing their offspring into the original
  population

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Coding a Genetic Aglorithm

• First consider the parameters of the problem
• Use binary numbers to represent each parameter
• Others have suggested using a user defined
  language to encode the problem
• Once the parameters are established, generate a
  random initial population

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Fitness Fuction

• It is analogous to the environment an animal
  lives in
• Gives a numerical description of how fit the
  solution encoded in a particular chromosome is.
• Penalty Functions
• Approximate Function evaluation

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Issues With Fitness Functions

• Premature convergence
• When a super fit (although not optimal)
  chromosome dominates the population
• This chromosome usually represents a local
  maximum
• Makes it impossible to use fitness alone as an
  indicator of reproductive potential
• Slow finishing
• When the populations have a high average fitness
  and dont have the extra oomph to push further
  and find a maximum

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Selecting a Mate

• Parents Selection Techniques
• Explicit fitness remapping
• Fitness scaling
• Fitness windowing
• Fitness ranking
• Implicit fitness remapping
• Use tournaments to choose parents

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Crossover Reproduction

• 1-point crossover
• Two mating chromosomes are cut at one point and
  the cuts are exchanged between the two parents.

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Cross Over Reproduction

• 2-point crossover
• Instead of a linear string, think of the
  chromosome as a loop formed by joining both ends.
  
• To mate, just cut a section in both parent loops
  and exchange missing sections
• Is preferred over 1-point crossover because it
  allows one to search the problem space more
  thoroughly

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Crossover Reproduction

• Uniform crossover
• A randomly generated cross over mask is created
  for each pair of parents.
• Based on the mask, the parents copy their genes
  to create new offspring.
• Where there is a 1, parent 1 copies its gene
• Where there is a 0, parent 2 copies its gene

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Introducing Offspring Into the Population

• In most genetic algorithm examples, the whole
  population is replaced with the offspring
• The generation gap is 1
• In the insect world, parents die soon after the
  eggs are laid

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Introducing Offspring Into the Population

• Steady-state
• Inspired by mammals and other long lived
  creatures.
• The offspring must compete with themselves and
  with their parents
• The steady-state technique require that an
  unlucky group of parents must die off to make
  room for the offspring

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Steady-State case

• Possible methods for choosing which parents will
  meet their demise
• Select parents according to fitness, and select
  random offspring to replace them.
• Select parents at random, and use fitness to
  choose offspring.
• Select both according to their fitness

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Applications for Genetic Algorithms

• Various medical applications, such as image
  segmentation and modeling.

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Robotic Applications

• Genetic Algorithms can be used to teach robots
  how to move.
• Brandeis University made a robot mother who
  created offspring using genetic algorithms
• One of her offspring is shown in the picture