- a population of 1000; [think about local maximum- could be a problem for sparse samples across the set]
- some evaluation to score each individual/ fitness function.s
- kill the ones underscore certain threshold;
- 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 - 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
