algogen

Tries to optimize the maximum multipoint loglikelihood using a dedicated genetic algorithm.

Synopsis :

The CarthaGene algogen command is invoked as either one of :

• lelacommande Options
• lelacommande NbGen NbMap SelType ProbaCross ProbaMut EvolFitn

Description :

The genetic algorithm tries to optimize the loglikelihood of orders using a simulation of mating, mutation and selection by fitness. The initial population is defined by the best maps stored in the heap or from generated maps.

As the algorithm runs, ``+'' are printed on a generation such that a map is found that improves over the best map known.

Arguments :

• Options : -u to obtain the synopsys of the normal use, -h to print a one line description, -H to print a short help.
• The Nbgen argument correspond to the number of generations simulated.
• The NbMap argument correspond to the size of the population. If NbMap is set to 0, the command uses the heap to initialize the first generation population.
• The SelType argument specifies the type of selection used (0 for the roulette wheel, 1 for the stochastic remainder without replacement).
• The ProbaCross argument correspond to the probability of crossing-over. A typical value is 60 %.
• The ProbaMut argument correspond to the probability of mutation. A typical value is 5 to 10 %.
• The EvolFitn is a flag to set the evolutive fitness (0 or 1) which mean that selection becomes harder and harder with generations.

Returns :

nothing, all maps explored are candidate for being stored in the heap.

Example :

# we load a dataset
CG> dsload Data/rh.cg
{1 haploid RH 53 118 /homes/thomas/carthagene/test/Data/rh.cg}
# perform linkage group detection (output omitted)
CG> group 0.3 3
...
# we select the group 10
CG> mrkselset [groupget 10]

# put one (stupid) map in the heap using the 'sem' command
CG> sem

Map -1 : log10-likelihood =  -161.87
-------:
 Set : Marker List ...
   1 : G5 G18 G17 G14 G16 G13 G12 G6 G7

# launch the 'algogen' command
CG> algogen 10 8 1 0.6 0.1 1
+
-
-
-
-
-
-
-
-
-

# check the reliability of the result with a large flip
CG> flips 9 0 0

Single Flip(window size : 9, threshold : 0.00).


Map -1 : log10-likelihood =  -141.81
-------:
 Set : Marker List ...
   1 : G7 G12 G13 G16 G6 G5 G17 G18 G14


 2 2 2 3 2 1 3 3 2  log10
 1 6 7 0 0 9 1 2 8   -141.81
[8 7 6 5 - 3 2 1 0]      0.00


# the map was optimal

See also:

• greedy (2.6.8)
• annealing (2.6.7)
• flips (2.6.4)
• polish (2.6.5)