Introduction to Genetic Algorithms and Evolutionary Computation - PowerPoint PPT Presentation

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Introduction to Genetic Algorithms and Evolutionary Computation

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Introduction to Genetic Algorithms and Evolutionary Computation Andrew L. Nelson Visiting Research Faculty University of South Florida * 2/9/2004 Genetic Algorithms ... – PowerPoint PPT presentation

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Title: Introduction to Genetic Algorithms and Evolutionary Computation


1
Introduction to Genetic Algorithms and
Evolutionary Computation
  • Andrew L. Nelson
  • Visiting Research Faculty
  • University of South Florida

2
Overview
  • Outline to the left
  • Current topic in red
  • Introduction
  • Algorithm Formulation
  • Example
  • Case Study
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

3
References
  • Holland, J. J., Adaptation in Natural and
    Artificial Systems, University of Michigan Press,
    Ann Arbor Michigan, 1975.
  • D.B. Fogel, Evolutionary Computation, Toward a
    New Philosophy of Machine Intelligence, 2nd Ed.,
    IEEE Press, Piscataway, NJ, 2000.
  • M. Mitchell, An Introduction to Genetic
    Algorithms, The MIT Press, Cambridge,
    Massachusetts, 1998.
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

4
Introduction
  • Genetic Algorithms
  • Base on Natural Evolution
  • Stochastic Optimization
  • Stochastic Numerical Techniques
  • Evolutionary Computation
  • Artificial Life
  • Machine Learning
  • Artificial Evolution
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

5
Introduction
  • Population of candidate solutions
  • Evaluate the quality of each solution
  • Survival (and reproduction) of the fittest
  • Crossover and Mutation
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

6
Sample Application Domain
  • Finding the best path between two points in "Grid
    World"
  • Creatures in world
  • Occupy a single cell
  • Can move to neighboring cells
  • Goal Travel from the gray cell to the green cell
    in the shortest number of steps
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

7
Algorithm Formulation
  • Components of a Genetic Algorithm
  • Genome
  • Fitness metric
  • Stochastic modification
  • Cycles of generations
  • Many variations
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

8
Genome
  • The genome is used represent candidate solutions
  • Fixed length Bitstrings
  • Holland
  • Traditional
  • Convergence theorems exist
  • Real-valued genomes
  • Artificial evolution
  • Difficult to prove convergence
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

9
Genome
  • Example Representation of a path through a
    square maze
  • Representation N00, E10, S11,W01
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

10
Population
  • Population, P is made up of individuals pn where
    N is the population size
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

11
Fitness Function
  • F(p) called Objective Function
  • Example Shortest legal path to goal
  • F(pn) S(steps)
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

12
Selection
  • Selection Methods of selection of the parents of
    the next generation of candidate solutions
  • Diverse methods
  • Probabilistic
  • Chance of be selected is proportional to fitness
  • Greedy
  • the fittest solutions are selected
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

13
Propagation
  • The next generation is generated from the fittest
    members of the current population
  • Genetic operators
  • Crossover (recombination)
  • Mutation
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

14
Propagation Crossover
  • Example 1 point crossover
  • Two parents generate 1 offspring
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

15
Propagation Mutation
  • Example Bitstring point mutation
  • Replace randomly selected bits with their
    complements
  • One parent generates one offspring
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

16
Worked Example
  • World size
  • 4X4
  • Population size
  • N 4
  • Genome
  • 16 bits
  • Fitness
  • F(p) (8-Steps before reaching goal)
    (squares from goal)
  • Propagation Greedy, Elitist
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

17
Ex Initial Population
  • Initial Population P(0) 4 random 16-bit strings
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

18
Ex Fitness Calculation
  • Fitness calculations
  • F(p1) (8-8) 4 -4
  • F(p2) -5
  • F(p3) -6
  • F(p4) -4
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

19
Ex Selection and Propagation
  • Select p1 and p4 as parents of the next
    generation, P(1)
  • Produce offspring using crossover and mutation
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

20
Ex Book Keeping...
  • The next generation is...
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

21
Ex Repeat for next Generation
  • Repeat
  • F(p1) -4
  • F(p2) -4
  • F(p3) 0
  • F(p4) -4
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers

22
Case Study
  • References
  • Introduction
  • Sample Application
  • Formulation
  • Genome
  • Population
  • Fitness Function
  • Selection
  • Propagation
  • Worked Example
  • Case Study Evolving Neural Controllers
  • Evolution of neural networks for autonomous robot
    control using competitive relative fitness
    evaluation
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