Introduction to Genetic Algorithms

1
Introduction to Genetic Algorithms

• Jeff Plummer
• Nov. 2003

2
Background

• Problems are solved by an evolutionary process
  resulting in a best (fittest) solution
  (survivor).
• Evolutionary Computing
• 1960s by I. Rechenberg
• Genetic Algorithms
• Invented by John Holland 1975
• Made popular by John Koza 1992

3
How GA are Different than Traditional Search
Methods

• GAs work with a coding of the parameter set, not
  the parameters themselves.
• GAs search from a population of points, not a
  single point.
• GAs use payoff information, not derivatives or
  auxiliary knowldege.
• GAs use probablistic transition rules, not
  deterministic rules.

4
Vocabulary

• Gene An single encoding of part of the solution
  space.
• Chromosome A string of Genes that represents
  a solution.
• Population - The number of Chromosomes
  available to test.

5
Simple Example

• f(x) MAX(x2) 0 lt x lt 32
• Encode Solution Just use 5 bits (1 or 0).
• Generate initial population.
• Evaluate each solution against objective.

6
Simple Example (cont.)

• Create next generation of solutions
• Probability of being a parent depends on the
  fitness.
• Ways for parents to create next generation
• Reproduction
• Use a string again unmodified.
• Crossover
• Cut and paste portions of one string to another.
• Mutation
• Randomly flip a bit.
• COMBINATION of all of the above.

7
The Basic Genetic Algorithm

• Start Generate random population of n
  chromosomes (suitable solutions for the problem)
• Fitness Evaluate the fitness f(x) of each
  chromosome x in the population
• New population Create a new population by
  repeating following steps until the new
  population is complete
• Selection Select two parent chromosomes from a
  population according to their fitness (the better
  fitness, the bigger chance to be selected)
• Crossover With a crossover probability cross
  over the parents to form new offspring
  (children). If no crossover was performed,
  offspring is the exact copy of parents.
• Mutation With a mutation probability mutate new
  offspring at each locus (position in chromosome).
  
• Accepting Place new offspring in the new
  population
• Replace Use new generated population for a
  further run of the algorithm
• Test If the end condition is satisfied, stop,
  and return the best solution in current
  population
• Loop Go to step 2

8
Example Applet

• http//www.rennard.org/alife/english/gavintrgb.htm
  l

9
GAs and Games

• GAs are learning systems
• Consider a Space Ship AI - Evade
• Chromosome encoding is string of Up, Down, Left,
  Right, Accelerate, Decelerate
• Fitness of Chromosome is proportional to the
  length of time it can evade a player.
• As player plays, ships get better at evading.
• DOESNT REQUIRE A LIVE PLAYER!!!!!!

10
Bibliography

• Genetic Algorithms in Search, Optimization, and
  Machine Learning by David E. Goldberg.
• http//cs.felk.cvut.cz/xobitko/ga/
• http//www.sambee.co.th/MazeSolver/mazega.htm
• http//www.rennard.org/alife/english/gavintrgb.htm
  l