Square Root of Negative One

generating organic form

Posted in algorithm, visual by cheng on February 7, 2010

Visual Models of Morphogenesis (Przemyslaw Prusinkiewicz et al.)

Grow more plants with algorithm

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


“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