/usr/share/octave/packages/ga-0.10.0/doc-cache is in octave-ga 0.10.0-1.
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# name: cache
# type: cell
# rows: 3
# columns: 9
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 18
crossoverscattered
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 135
simplified example (nvars == 4)
p1 = [varA varB varC varD]
p2 = [var1 var2 var3 var4]
b = [1 1 0 1]
child = [varA varB var3 varD]
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
simplified example (nvars == 4)
p1 = [varA varB varC varD]
p2 = [var1 var2 va
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 14
fitscalingrank
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 71
TODO
ranks ([7,2,2]) == [3.0,1.5,1.5]
is [3,1,2] (or [3,2,1]) useful?
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 27
TODO
ranks ([7,2,2]) == [3.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 2
ga
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1866
-- Function File: X = ga (FITNESSFCN, NVARS)
-- Function File: X = ga (FITNESSFCN, NVARS, A, B)
-- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ)
-- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB)
-- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB,
NONLCON)
-- Function File: X = ga (FITNESSFCN, NVARS, A, B, AEQ, BEQ, LB, UB,
NONLCON, OPTIONS)
-- Function File: X = ga (PROBLEM)
-- Function File: [X, FVAL] = ga (...)
-- Function File: [X, FVAL, EXITFLAG] = ga (...)
-- Function File: [X, FVAL, EXITFLAG, OUTPUT] = ga (...)
-- Function File: [X, FVAL, EXITFLAG, OUTPUT, POPULATION] = ga (...)
-- Function File: [X, FVAL, EXITFLAG, OUTPUT, POPULATION, SCORES] = ga
(...)
Find minimum of function using genetic algorithm.
*Inputs*
FITNESSFCN
The objective function to minimize. It accepts a vector X of
size 1-by-NVARS, and returns a scalar evaluated at X.
NVARS
The dimension (number of design variables) of FITNESSFCN.
OPTIONS
The structure of the optimization parameters; can be created
using the `gaoptimset' function. If not specified, `ga'
minimizes with the default optimization parameters.
PROBLEM
A structure containing the following fields:
* `fitnessfcn'
* `nvars'
* `Aineq'
* `Bineq'
* `Aeq'
* `Beq'
* `lb'
* `ub'
* `nonlcon'
* `randstate'
* `randnstate'
* `solver'
* `options'
*Outputs*
X
The local unconstrained found minimum to the objective
function, FITNESSFCN.
FVAL
The value of the fitness function at X.
See also: gaoptimset
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 49
Find minimum of function using genetic algorithm.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 17
gacreationuniform
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 487
-- Function File: POPULATION = gacreationuniform (GENOMELENGTH,
FITNESSFCN, OPTIONS)
Create a random initial population with a uniform distribution.
*Inputs*
GENOMELENGTH
The number of indipendent variables for the fitness function.
FITNESSFCN
The fitness function.
OPTIONS
The options structure.
*Outputs*
POPULATION
The initial population for the genetic algorithm.
See also: ga, gaoptimset
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 63
Create a random initial population with a uniform distribution.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 10
gaoptimset
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1242
-- Function File: OPTIONS = gaoptimset
-- Function File: OPTIONS = gaoptimset ('PARAM1', VALUE1, 'PARAM2',
VALUE2, ...)
Create genetic algorithm options structure.
*Inputs*
PARAM
Parameter to set. Unspecified parameters are set to their
default values; specifying no parameters is allowed.
VALUE
Value of PARAM.
*Outputs*
OPTIONS
Structure containing the options, or parameters, for the
genetic algorithm.
*Options*
`CreationFcn'
`CrossoverFcn'
`CrossoverFraction'
`EliteCount'
`FitnessLimit'
`FitnessScalingFcn'
`Generations'
`InitialPopulation'
Can be partial.
`InitialScores'
column vector | [] (default) . Can be partial.
`MutationFcn'
`PopInitRange'
`PopulationSize'
`SelectionFcn'
`TimeLimit'
`UseParallel'
"always" | "never" (default) . Parallel evaluation of
objective function. TODO: parallel evaluation of nonlinear
constraints
`Vectorized'
"on" | "off" (default) . Vectorized evaluation of objective
function. TODO: vectorized evaluation of nonlinear constraints
See also: ga
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 43
Create genetic algorithm options structure.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 16
mutationgaussian
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 30
start mutationgaussian logic
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 30
start mutationgaussian logic
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 13
rastriginsfcn
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 69
-- Function File: Y = rastriginsfcn (X)
Rastrigin's function.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 21
Rastrigin's function.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 18
selectionstochunif
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 104
fix an entry of the steps (or parents) vector
assert (steps(1, index_steps) < max_step_size); ## DEBUG
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
fix an entry of the steps (or parents) vector
assert (steps(1, index_steps) < m
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
test_ga
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 69
-- Script File: test_ga
Execute all available tests at once.
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 36
Execute all available tests at once.
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