Genetic Algorithms Genetic Programming

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Genetic AlgorithmsGenetic Programming

• Ata Kaban
• A.Kaban_at_cs.bham.ac.uk
• School of Computer Science
• University of Birmingham
• 2003

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• Genetic algorithms
• Genetic programming
• Evolutionary computation

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Evolutionary Computation

• Computation procedures patterned after biological
  evolution
• Search procedures that probabilistically applies
  search operators to a set of points in the search
  space

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The Evolutionary Cycle
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When are useful

• Hard discrete optimisation problems when the
  search space is very large
• E.g. choosing the NN topology
• Very hard continuous optimisation problems
• Evolutionary simulations

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Genetic algorithms (inventors)

• John Holland Adaptation in Natural and
  Artificial Systems, University of Michigan Press
  (1975)- Genetic Algorithms
• Lawrence Fogel, M. Evans, M. Walsh Artificial
  Intelligence through Simulated Evolution, Wiley,
  1966 - Evolutionary programming
• Ingo Rechenburg, 1965 - Evolutionary Strategies

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Machine Learning and Genetic Algorithms

• Most Machine Learning is concerned with
  constructing a hypothesis from examples that
  generalises well
• fast but very biased
• GA is a discovery-search over hypotheses
• slow and unbiased

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The Genetic Algorithm

• 1. Create a population of encoded potential
  solutions (chromosomes)
• 2. Evaluate the fitness of all the chromosomes
• 3. Select fitter chromosomes to form new
  candidate population
• 4. Form new candidate population by recombining
  genes from candidate population
• 5. Mutate
• 6. Until satisfied go to 2

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Representing hypotheses as strings (chromosomes)

• E.g.
• Outlook?Sunny, Overcast, Rain,
• Wind ? Strong, Week
• PlayTennis ?Yes, No
• Represent
• (Overcast V Rain)?(WindStrong) by
• 011 10
• Represent
• IF WindStrong THEN PlayTennisyes by
• 111 10 10 or
• 111 10 1
• Can you figure out the rationale?

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• In the previous example
• Fixed length string representations for single
  rules
• The outcome should not be constrained (11 or 00
  for PlayTennis would not make sense)
• Designing a suitable string-based representation
  of hypothesis is not always as simple
• Much of the success of the GA will depend on
  doing this is a sensitive way.

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GA steps in more detail

• Operators for GA
• Crossover
• Mutation
• Selection

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Crossover

• Take the new candidate solutions after selection
  and recombine genetic material
• A b c D e f a B c D e f
• a B c D e F A b c D e F
• A b c D e f a B c D e F
• a B c D e F A b c D e f

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Mutation

• After the recombination stage, just randomly
  alter a few genes
• a B c D e F a B c D e F

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Selection

• Fitness numerical value returned by our
  criterion of ranking hypotheses
• Selection procedures
• Fitness proportionate selection
• Tournament selection (size 2)
• 1. choose two chromosomes h1 and h2 at random
• 2. promote the fitter of the two to the next
  candidate population
• 3. until new candidate population full go to 1.
• Many other possibilities! Can you figure out the
  rationale?

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Selection strategy

• We want to have some way to ensure that better
  individuals have a better chance of being parents
  then less good individuals
• This will give us selection pressure which will
  drive the population forward.
• We have to be careful to give less good
  individuals at least some chance of being parents
  they may include some useful genetic material
• What could go wrong with the Fitness
  proportionate selection procedure?

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What could go wrong with Fitness proportionate
selection?

• Danger of premature convergence because
  outstanding individuals take over the entire
  population very quickly
• Low selection pressure when fitness values are
  near each other

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An example find the maximal peak of the function
(difficult optimisation)
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Solution
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Expected behaviour of GA
generation
proportion
Fitness
Fitness
Generation
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Another example of tree-structured program
individual

• Need to define
• Terminals x,y,const
• Primitive functions sin, cos, ? , ,-, ()2

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An early simple example The Block problem
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The GA solution (learned program)

• Trained on 166 test problems
• Using a population of 300 programs
• After 10 generations
• The solution found by GA which solves all 166
  cases
• (EQ (DU (MT CS) (NOT CS))
• (DU (MS NN) (NOT NN)))
• What it does? Simple but it makes sense!

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Example Evolving programs on a mobile robot

• Goal obstacle avoidance
• inputs from eight sensors on robot s1-s8 with
  values between 0,1023 (higher values mean
  closer obstacle)
• output to two motors (speeds) m1,m2 with values
  between 0,15.

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Fitness function
Penalty
Reward (going straight)
Reward (going fast)
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Results
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Electronic Filter Circuit Design

• Individuals are programs that transform beginning
  circuit to final circuit by
• Adding/subtracting components and connections
• Fitness computed by simulating the circuit
• Population of 640,000 has been run on a parallel
  processor
• After 137 generations, the discovered circuits
  exhibited performance competitive with best human
  designs

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Summary

• Evolutionary programming conducts randomised
  parallel search through the hypothesis space
• Approaches learning as an optimisation problem
  (optimise fitness)
• Evaluation of fitness can be very indirect
• Nice metaphor with Darwinian theory of biological
  evolution
• Little theoretical justification for many of the
  heuristics