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

3. References
References
Introduction
Sample Application
Formulation
Genome
Population
Fitness Function
Selection
Propagation
Worked Example
Case Study: Evolving Neural Controllers
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.
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Genetic Algorithms

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

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

6. Sample Application Domain
References
Introduction
Sample Application
Formulation
Genome
Population
Fitness Function
Selection
Propagation
Worked Example
Case Study: Evolving Neural Controllers
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
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Genetic Algorithms

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

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

9. Genome Example: Representation of a path through a square maze:
References
Introduction
Sample Application
Formulation
Genome
Population
Fitness Function
Selection
Propagation
Worked Example
Case Study: Evolving Neural Controllers
Example: Representation of a path through a square maze:
Representation: N=00, E=10, S=11,W=01
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Genetic Algorithms

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

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

12. Selection
References
Introduction
Sample Application
Formulation
Genome
Population
Fitness Function
Selection
Propagation
Worked Example
Case Study: Evolving Neural Controllers
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
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Genetic Algorithms

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

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

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

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

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

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
2/9/2004
Genetic Algorithms

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
2/9/2004
Genetic Algorithms

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
The next generation is...
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Genetic Algorithms

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

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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Genetic Algorithms