Square Root of Negative One

generative algorithm

Posted in algorithm by cheng on February 3, 2010

GOLAN lecture

  1. a population of 1000; [think  about local maximum- could be a problem for sparse samples across the set]
  2. some evaluation to score each individual/ fitness function.s
  3. kill the ones underscore certain threshold;
  4. the survivor breed offspring. HOW?
    • the likely hood of mutation M, say 3%
    • crossover C, 5%
    • parametric representation of the individuals.
      0000 01110 | 0000 0011
  5. repopulate the died portion

BOOK an introduction to genetic Algorithms (complex adaptive systems)

BRIAN SCAZ lecture

Outline of the Basic Genetic Algorithm

1.[Start] Generate random population of n chromosomes
2.[Fitness] Evaluate the fitness of each chromosome
3.[New population] Create a new population by repeating:
A.[Selection] Select two parent chromosomes based on their fitness
B.[Crossover] With a crossover probability cross over the parents to
form new offspring (children). If no crossover was performed,
offspring is an exact copy of parents.
C.[Mutation] With a mutation probability mutate new offspring at
each locus (position in chromosome).
D.[Accepting] Place new offspring in a new population
4.[Replace] Use new generated population for a further run of algorithm
5.[Test] If the end condition is satisfied, stop, and return the best
solution in current population
6.[Loop] Go to step 2

KARL SIMS

“Evolving 3D Morphology and Behavior by Competition ”

  • Evolving Morphology with Control , i.e. body and brain
  • Evolution at times involves more than competition with the environment
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