R Under development (unstable) (2025-12-31 r89265) -- "Unsuffered Consequences"
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> pkgname <- "GA"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('GA')
Loading required package: foreach
Loading required package: iterators
Package 'GA' version 3.2.4
Type 'citation("GA")' for citing this R package in publications.

Attaching package: ‘GA’

The following object is masked from ‘package:utils’:

    de

> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("binary2decimal")
> ### * binary2decimal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: binary2decimal
> ### Title: Binary encoding of decimal numbers and vice versa.
> ### Aliases: binary2decimal decimal2binary
> 
> ### ** Examples
> 
> # for integer values
> dval <- 12
> (bval <- decimal2binary(dval))
[1] 1 1 0 0
> binary2decimal(bval)
[1] 12
> 
> # for real values
> dval <- 12.456
> # use
> (bval <- decimal2binary(dval*1000))
 [1] 1 1 0 0 0 0 1 0 1 0 1 0 0 0
> binary2decimal(bval)/1000
[1] 12.456
> 
> 
> 
> cleanEx()
> nameEx("binary2gray")
> ### * binary2gray
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: binary2gray
> ### Title: Gray encoding for binary strings
> ### Aliases: binary2gray gray2binary
> 
> ### ** Examples
> 
> # Consider a five-bit encoding of values 15 and 16  using the standard 
> # binary coding
> decimal2binary(15, 5)
[1] 0 1 1 1 1
> decimal2binary(16, 5)
[1] 1 0 0 0 0
> # Moving from 15 to 16 (or vice versa) all five bits need to be changed,
> # but using Gray encoding the two binary strings differ by one bit.
> binary2gray(decimal2binary(15, 5))
[1] 0 1 0 0 0
> binary2gray(decimal2binary(16, 5))
[1] 1 1 0 0 0
> 
> 
> 
> cleanEx()
> nameEx("de")
> ### * de
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: de
> ### Title: Differential Evolution via Genetic Algorithms
> ### Aliases: de show,de-method print,de-method
> ### Keywords: optimize
> 
> ### ** Examples
> 
> # 1) one-dimensional function
> f <- function(x)  abs(x)+cos(x)
> curve(f, -20, 20)
> 
> DE <- de(fitness = function(x) -f(x), lower = -20, upper = 20)
> plot(DE)
> summary(DE)
── Differential Evolution ────────────── 

DE settings: 
Type                  =  real-valued 
Population size       =  10 
Number of generations =  100 
Elitism               =  0 
Stepsize              =  0.8 
Crossover probability =  0.5 
Mutation probability  =  0 
Search domain = 
       x1
lower -20
upper  20

DE results: 
Iterations             = 100 
Fitness function value = -1 
Solution = 
     x1
[1,]  0
> 
> curve(f, -20, 20, n = 1000)
> abline(v = DE@solution, lty = 3)
> 
> # 2) "Wild" function, global minimum at about -15.81515
> 
> wild <- function(x) 10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x + 80
> plot(wild, -50, 50, n = 1000)
> 
> # from help("optim")
> SANN <- optim(50, fn = wild, method = "SANN",
+               control = list(maxit = 20000, temp = 20, parscale = 20))
> unlist(SANN[1:2])
     par    value 
-15.6616  67.4703 
> 
> DE <- de(fitness = function(...) -wild(...), lower = -50, upper = 50)
> plot(DE)
> summary(DE)
── Differential Evolution ────────────── 

DE settings: 
Type                  =  real-valued 
Population size       =  10 
Number of generations =  100 
Elitism               =  0 
Stepsize              =  0.8 
Crossover probability =  0.5 
Mutation probability  =  0 
Search domain = 
       x1
lower -50
upper  50

DE results: 
Iterations             = 100 
Fitness function value = -67.65118 
Solution = 
            x1
[1,] -15.65693
> 
> # 3) two-dimensional Rastrigin function
> 
> Rastrigin <- function(x1, x2)
+ {
+   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
+ }
> 
> x1 <- x2 <- seq(-5.12, 5.12, by = 0.1)
> f <- outer(x1, x2, Rastrigin)
> persp3D(x1, x2, f, theta = 50, phi = 20, col.palette = bl2gr.colors)
> 
> DE <- de(fitness = function(x) -Rastrigin(x[1], x[2]),
+          lower = c(-5.12, -5.12), upper = c(5.12, 5.12),
+          popSize = 50)
> plot(DE)
> summary(DE)
── Differential Evolution ────────────── 

DE settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  0 
Stepsize              =  0.8 
Crossover probability =  0.5 
Mutation probability  =  0 
Search domain = 
         x1    x2
lower -5.12 -5.12
upper  5.12  5.12

DE results: 
Iterations             = 100 
Fitness function value = -9.222845e-12 
Solution = 
                x1           x2
[1,] -1.476754e-07 1.571135e-07
> 
> filled.contour(x1, x2, f, color.palette = bl2gr.colors,
+                plot.axes = { axis(1); axis(2); 
+                              points(DE@solution, 
+                                     col = "yellow", pch = 3, lwd = 2) })
> 
> # 4) two-dimensional Ackley function
> 
> Ackley <- function(x1, x2)
+ {
+   -20*exp(-0.2*sqrt(0.5*(x1^2 + x2^2))) - 
+   exp(0.5*(cos(2*pi*x1) + cos(2*pi*x2))) + exp(1) + 20
+ }
> 
> x1 <- x2 <- seq(-3, 3, by = 0.1)
> f <- outer(x1, x2, Ackley)
> persp3D(x1, x2, f, theta = 50, phi = 20, col.palette = bl2gr.colors)
> 
> DE <- de(fitness = function(x) -Ackley(x[1], x[2]),
+          lower = c(-3, -3), upper = c(3, 3),
+          stepsize = NA)
> plot(DE)
> summary(DE)
── Differential Evolution ────────────── 

DE settings: 
Type                  =  real-valued 
Population size       =  20 
Number of generations =  100 
Elitism               =  0 
Stepsize              =  runif(0.5,1.0) 
Crossover probability =  0.5 
Mutation probability  =  0 
Search domain = 
      x1 x2
lower -3 -3
upper  3  3

DE results: 
Iterations             = 100 
Fitness function value = -2.488335e-09 
Solution = 
               x1           x2
[1,] 3.082543e-10 8.239874e-10
> 
> filled.contour(x1, x2, f, color.palette = bl2gr.colors,
+                plot.axes = { axis(1); axis(2); 
+                              points(DE@solution, 
+                                     col = "yellow", pch = 3, lwd = 2) })
>                                     
> # 5) Curve fitting example (see Scrucca JSS 2013)
> 
> ## Not run: 
> ##D # subset of data from data(trees, package = "spuRs")
> ##D tree <- data.frame(Age = c(2.44, 12.44, 22.44, 32.44, 42.44, 52.44, 62.44, 
> ##D                            72.44, 82.44, 92.44, 102.44, 112.44),
> ##D                    Vol = c(2.2, 20, 93, 262, 476, 705, 967, 1203, 1409, 
> ##D                            1659, 1898, 2106))
> ##D richards <- function(x, theta) 
> ##D   { theta[1]*(1 - exp(-theta[2]*x))^theta[3] }
> ##D fitnessL2 <- function(theta, x, y) 
> ##D   { -sum((y - richards(x, theta))^2) }
> ##D DE <- de(fitness = fitnessL2, x = tree$Age, y = tree$Vol, 
> ##D          lower = c(3000, 0, 2), upper = c(4000, 1, 4),
> ##D          popSize = 500, maxiter = 1000, run = 100, 
> ##D          names = c("a", "b", "c"))
> ##D summary(DE)
> ## End(Not run)
> 
> 
> 
> 
> cleanEx()
> nameEx("ga")
> ### * ga
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ga
> ### Title: Genetic Algorithms
> ### Aliases: ga show,ga-method print,ga-method
> ### Keywords: optimize
> 
> ### ** Examples
> 
> # 1) one-dimensional function
> f <- function(x)  abs(x)+cos(x)
> curve(f, -20, 20)
> 
> fitness <- function(x) -f(x)
> GA <- ga(type = "real-valued", fitness = fitness, lower = -20, upper = 20)
> summary(GA)
── Genetic Algorithm ─────────────────── 

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
       x1
lower -20
upper  20

GA results: 
Iterations             = 100 
Fitness function value = -1.000022 
Solution = 
               x1
[1,] 2.239645e-05
> plot(GA)
> 
> curve(f, -20, 20)
> abline(v = GA@solution, lty = 3)
> 
> # 2) one-dimensional function
> f <- function(x)  (x^2+x)*cos(x) # -10 < x < 10
> curve(f, -10, 10)
> 
> # write your own tracing function
> monitor <- function(obj) 
+ { 
+   curve(f, -10, 10, main = paste("iteration =", obj@iter))
+   points(obj@population, obj@fitness, pch = 20, col = 2)
+   rug(obj@population, col = 2)
+   Sys.sleep(0.2)
+ }
> ## Not run: 
> ##D GA <- ga(type = "real-valued", fitness = f, lower = -10, upper = 10, monitor = monitor)
> ## End(Not run)
> # or if you want to suppress the tracing
> GA <- ga(type = "real-valued", fitness = f, lower = -10, upper = 10, monitor = NULL)
> summary(GA)
── Genetic Algorithm ─────────────────── 

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
       x1
lower -10
upper  10

GA results: 
Iterations             = 100 
Fitness function value = 47.70562 
Solution = 
           x1
[1,] 6.560536
> 
> monitor(GA)
> abline(v = GA@solution, lty = 3)
> 
> # 3) two-dimensional Rastrigin function
> 
> Rastrigin <- function(x1, x2)
+ {
+   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
+ }
> 
> x1 <- x2 <- seq(-5.12, 5.12, by = 0.1)
> f <- outer(x1, x2, Rastrigin)
> persp3D(x1, x2, f, theta = 50, phi = 20, col.palette = bl2gr.colors)
> filled.contour(x1, x2, f, color.palette = bl2gr.colors)
> 
> GA <- ga(type = "real-valued", fitness =  function(x) -Rastrigin(x[1], x[2]),
+          lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
+          popSize = 50, maxiter = 100)
> summary(GA)
── Genetic Algorithm ─────────────────── 

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
         x1    x2
lower -5.12 -5.12
upper  5.12  5.12

GA results: 
Iterations             = 100 
Fitness function value = -7.889556e-06 
Solution = 
              x1            x2
[1,] -0.00015417 -0.0001264876
> plot(GA)
> 
> # 4) Parallel GA
> # Simple example of an expensive fitness function obtained artificially by
> # introducing a pause statement. 
> ## Not run: 
> ##D Rastrigin <- function(x1, x2)
> ##D {
> ##D   Sys.sleep(0.1)
> ##D   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
> ##D }
> ##D 
> ##D system.time(GA1 <- ga(type = "real-valued", 
> ##D                       fitness =  function(x) -Rastrigin(x[1], x[2]),
> ##D                       lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
> ##D                       popSize = 50, maxiter = 100, monitor = FALSE,
> ##D                       seed = 12345))
> ##D 
> ##D system.time(GA2 <- ga(type = "real-valued", 
> ##D                       fitness =  function(x) -Rastrigin(x[1], x[2]),
> ##D                       lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
> ##D                       popSize = 50, maxiter = 100, monitor = FALSE,
> ##D                       seed = 12345, parallel = TRUE))
> ## End(Not run)
> 
> # 5) Hybrid GA
> # Example of GA with local search 
> 
> Rastrigin <- function(x1, x2)
+ {
+   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
+ }
> 
> GA <- ga(type = "real-valued", 
+          fitness =  function(x) -Rastrigin(x[1], x[2]),
+          lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
+          popSize = 50, maxiter = 100,
+          optim = TRUE)
/data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/minus.h:336:15: runtime error: signed integer overflow: 51 - -2147483648 cannot be represented in type 'typename storage_type<13>::type' (aka 'int')
    #0 0x7fc79b96e933 in Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>>::operator[](long) const /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/minus.h:336:15
    #1 0x7fc79b96e933 in void Rcpp::Vector<13, Rcpp::PreserveStorage>::import_expression<Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>>>(Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>> const&, long) /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:1082:9
    #2 0x7fc79b91ad63 in void Rcpp::Vector<13, Rcpp::PreserveStorage>::assign_object<Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>>>(Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>> const&, Rcpp::traits::integral_constant<bool, true>) /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:1051:9
    #3 0x7fc79b91ad63 in Rcpp::Vector<13, Rcpp::PreserveStorage>& Rcpp::Vector<13, Rcpp::PreserveStorage>::operator=<Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>>>(Rcpp::sugar::Minus_Primitive_Vector<13, true, Rcpp::Vector<13, Rcpp::PreserveStorage>> const&) /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:245:9
    #4 0x7fc79b91ad63 in rank_asR(Rcpp::Vector<14, Rcpp::PreserveStorage>, bool) /data/gannet/ripley/R/packages/tests-clang-UBSAN/GA/src/genope.cpp:21:23
    #5 0x7fc79b9427a0 in optimProbsel_Rcpp(Rcpp::Vector<14, Rcpp::PreserveStorage>, double) /data/gannet/ripley/R/packages/tests-clang-UBSAN/GA/src/genope.cpp:1703:39
    #6 0x7fc79b90eb09 in _GA_optimProbsel_Rcpp /data/gannet/ripley/R/packages/tests-clang-UBSAN/GA/src/RcppExports.cpp:466:34
    #7 0x55de87130e53 in R_doDotCall (/data/gannet/ripley/R/R-clang/bin/exec/R+0x8ee53)
    #8 0x55de8717fdaf in bcEval_loop eval.c
    #9 0x55de87168beb in bcEval eval.c
    #10 0x55de87168374 in Rf_eval (/data/gannet/ripley/R/R-clang/bin/exec/R+0xc6374)
    #11 0x55de87185768 in R_execClosure eval.c
    #12 0x55de87184c6b in applyClosure_core eval.c
    #13 0x55de871687c5 in Rf_eval (/data/gannet/ripley/R/R-clang/bin/exec/R+0xc67c5)
    #14 0x55de8718ad6d in do_set (/data/gannet/ripley/R/R-clang/bin/exec/R+0xe8d6d)
    #15 0x55de8716859f in Rf_eval (/data/gannet/ripley/R/R-clang/bin/exec/R+0xc659f)
    #16 0x55de871ba717 in Rf_ReplIteration (/data/gannet/ripley/R/R-clang/bin/exec/R+0x118717)
    #17 0x55de871bc21e in run_Rmainloop (/data/gannet/ripley/R/R-clang/bin/exec/R+0x11a21e)
    #18 0x55de871bc28a in Rf_mainloop (/data/gannet/ripley/R/R-clang/bin/exec/R+0x11a28a)
    #19 0x55de870a3da7 in main (/data/gannet/ripley/R/R-clang/bin/exec/R+0x1da7)
    #20 0x7fc7ac1135f4 in __libc_start_call_main (/lib64/libc.so.6+0x35f4) (BuildId: a1dda014206b55b07f58fe8db80121b752dc3d03)
    #21 0x7fc7ac1136a7 in __libc_start_main@GLIBC_2.2.5 (/lib64/libc.so.6+0x36a7) (BuildId: a1dda014206b55b07f58fe8db80121b752dc3d03)
    #22 0x55de870a3cc4 in _start (/data/gannet/ripley/R/R-clang/bin/exec/R+0x1cc4)

SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/gannet/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/minus.h:336:15 
> summary(GA)
── Genetic Algorithm ─────────────────── 

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
         x1    x2
lower -5.12 -5.12
upper  5.12  5.12

GA results: 
Iterations             = 100 
Fitness function value = 0 
Solution = 
     x1 x2
[1,]  0  0
> 
> 
> 
> 
> cleanEx()
> nameEx("gaControl")
> ### * gaControl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gaControl
> ### Title: A function for setting or retrieving defaults genetic operators
> ### Aliases: gaControl
> 
> ### ** Examples
> 
> # get and save defaults
> defaultControl <- gaControl()
> print(defaultControl)
$binary
$binary$population
[1] "gabin_Population"

$binary$selection
[1] "gabin_lrSelection"

$binary$crossover
[1] "gabin_spCrossover"

$binary$mutation
[1] "gabin_raMutation"


$`real-valued`
$`real-valued`$population
[1] "gareal_Population"

$`real-valued`$selection
[1] "gareal_lsSelection"

$`real-valued`$crossover
[1] "gareal_laCrossover"

$`real-valued`$mutation
[1] "gareal_raMutation"


$permutation
$permutation$population
[1] "gaperm_Population"

$permutation$selection
[1] "gaperm_lrSelection"

$permutation$crossover
[1] "gaperm_oxCrossover"

$permutation$mutation
[1] "gaperm_simMutation"


$eps
[1] 1.490116e-08

$useRcpp
[1] TRUE

> # get current defaults only for binary search
> gaControl("binary")
$population
[1] "gabin_Population"

$selection
[1] "gabin_lrSelection"

$crossover
[1] "gabin_spCrossover"

$mutation
[1] "gabin_raMutation"

> # set defaults for selection operator of binary search
> gaControl("binary" = list(selection = "gabin_tourSelection"))
> gaControl("binary")
$population
[1] "gabin_Population"

$selection
[1] "gabin_tourSelection"

$crossover
[1] "gabin_spCrossover"

$mutation
[1] "gabin_raMutation"

> # set defaults for selection and crossover operators of binary search
> gaControl("binary" = list(selection = "ga_rwSelection", 
+                           crossover = "gabin_uCrossover"))
> gaControl("binary")
$population
[1] "gabin_Population"

$selection
[1] "ga_rwSelection"

$crossover
[1] "gabin_uCrossover"

$mutation
[1] "gabin_raMutation"

> # restore defaults
> gaControl(defaultControl)
> gaControl()
$binary
$binary$population
[1] "gabin_Population"

$binary$selection
[1] "gabin_lrSelection"

$binary$crossover
[1] "gabin_spCrossover"

$binary$mutation
[1] "gabin_raMutation"


$`real-valued`
$`real-valued`$population
[1] "gareal_Population"

$`real-valued`$selection
[1] "gareal_lsSelection"

$`real-valued`$crossover
[1] "gareal_laCrossover"

$`real-valued`$mutation
[1] "gareal_raMutation"


$permutation
$permutation$population
[1] "gaperm_Population"

$permutation$selection
[1] "gaperm_lrSelection"

$permutation$crossover
[1] "gaperm_oxCrossover"

$permutation$mutation
[1] "gaperm_simMutation"


$eps
[1] 1.490116e-08

$useRcpp
[1] TRUE

> 
> 
> 
> cleanEx()
> nameEx("ga_pmutation")
> ### * ga_pmutation
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ga_pmutation
> ### Title: Variable mutation probability in genetic algorithms
> ### Aliases: ga_pmutation ga_pmutation_R ga_pmutation_Rcpp
> 
> ### ** Examples
> 
> ## Not run: 
> ##D Rastrigin <- function(x1, x2)
> ##D {
> ##D   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
> ##D }
> ##D 
> ##D GA <- ga(type = "real-valued", 
> ##D          fitness =  function(x) -Rastrigin(x[1], x[2]),
> ##D          lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
> ##D          popSize = 50, maxiter = 500, run = 100,
> ##D          pmutation = ga_pmutation)
> ##D plot(GA)
> ##D 
> ##D GA <- ga(type = "real-valued", 
> ##D          fitness =  function(x) -Rastrigin(x[1], x[2]),
> ##D          lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
> ##D          popSize = 50, maxiter = 500, run = 100,
> ##D          pmutation = function(...) ga_pmutation(..., p0 = 0.1))
> ##D plot(GA)
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("gaisl")
> ### * gaisl
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gaisl
> ### Title: Islands Genetic Algorithms
> ### Aliases: gaisl show,gaisl-method print,gaisl-method
> ### Keywords: optimize
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # two-dimensional Rastrigin function
> ##D Rastrigin <- function(x1, x2)
> ##D {
> ##D   20 + x1^2 + x2^2 - 10*(cos(2*pi*x1) + cos(2*pi*x2))
> ##D }
> ##D 
> ##D x1 <- x2 <- seq(-5.12, 5.12, by = 0.1)
> ##D f <- outer(x1, x2, Rastrigin)
> ##D persp3D(x1, x2, f, theta = 50, phi = 20)
> ##D filled.contour(x1, x2, f, color.palette = jet.colors)
> ##D 
> ##D GA <- gaisl(type = "real-valued", 
> ##D             fitness =  function(x) -Rastrigin(x[1], x[2]),
> ##D             lower = c(-5.12, -5.12), upper = c(5.12, 5.12), 
> ##D             popSize = 80, maxiter = 500, 
> ##D             numIslands = 4, migrationInterval = 50)
> ##D summary(GA)
> ##D plot(GA)
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("numericOrNA-class")
> ### * numericOrNA-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: numericOrNA-class
> ### Title: Virtual Class "numericOrNA" - Simple Class for sub-assignment
> ###   Values
> ### Aliases: numericOrNA-class
> ### Keywords: classes
> 
> ### ** Examples
> 
> showClass("numericOrNA")
Virtual Class "numericOrNA" [package "GA"]

No Slots, prototype of class "numeric"

Known Subclasses: 
Class "numeric", directly
Class "logical", directly
Class "integer", by class "numeric", distance 2
Class "double", by class "numeric", distance 2
Class "factor", by class "numeric", distance 3
Class "ordered", by class "numeric", distance 4
> 
> 
> 
> cleanEx()
> nameEx("palettes")
> ### * palettes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: palettes
> ### Title: Colours palettes
> ### Aliases: jet.colors spectral.colors bl2gr.colors
> ### Keywords: hplot
> 
> ### ** Examples
> 
> jet.colors(9)
[1] "#00007F" "#0000FF" "#007FFF" "#00FFFF" "#7FFF7F" "#FFFF00" "#FF7F00"
[8] "#FF0000" "#7F0000"
> spectral.colors(9)
[1] "#2B83BA" "#6BB0AF" "#ABDDA4" "#D4EEB1" "#FFFFBF" "#FED690" "#FDAE61"
[8] "#EA633E" "#D7191C"
> bl2gr.colors(9)
[1] "#073F80" "#0862A6" "#2582B9" "#42A4CB" "#67BFCB" "#8DD2BE" "#B1E0B8"
[8] "#CEECC7" "#DFF2DA"
> 
> par(mfrow = c(3,1), mar = c(1,1,1,1))
> n = 21
> image(1:21, 1, as.matrix(1:21), col = jet.colors(21),
+       ylab = "", xlab = "", xaxt = "n", yaxt = "n", bty = "n")
> image(1:21, 1, as.matrix(1:21), col = spectral.colors(21),
+       ylab = "", xlab = "", xaxt = "n", yaxt = "n", bty = "n")
> image(1:21, 1, as.matrix(1:21), col = bl2gr.colors(21),
+       ylab = "", xlab = "", xaxt = "n", yaxt = "n", bty = "n")
> 
> 
> 
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("persp3D")
> ### * persp3D
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: persp3D
> ### Title: Perspective plot with colour levels
> ### Aliases: persp3D
> ### Keywords: hplot
> 
> ### ** Examples
> 
> y <- x <- seq(-10, 10, length=60)
> f <- function(x,y) { r <- sqrt(x^2+y^2); 10 * sin(r)/r }
> z <- outer(x, y, f)
> persp3D(x, y, z, theta = 30, phi = 30, expand = 0.5)
> persp3D(x, y, z, col.palette = heat.colors, phi = 30, theta = 225, 
+         box = TRUE, border = NA, shade = .4)
> x1 <- seq(-3,3,length=50)
> x2 <- seq(-3,3,length=50)
> y <- function(x1, x2) sin(x1)+cos(x2)
> persp3D(x1, x2, outer(x1,x2,y), zlab="y", theta = 150, phi = 20, expand = 0.6)
> 
> 
> 
> cleanEx()
> nameEx("plot.de-method")
> ### * plot.de-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.de-method
> ### Title: Plot of Differential Evolution search path
> ### Aliases: plot,de-method
> ### Keywords: methods hplot
> 
> ### ** Examples
> 
> # See examples in help(de)
> 
> 
> 
> cleanEx()
> nameEx("plot.ga-method")
> ### * plot.ga-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.ga-method
> ### Title: Plot of Genetic Algorithm search path
> ### Aliases: plot,ga-method plot.ga
> ### Keywords: methods hplot
> 
> ### ** Examples
> 
> # See examples in help(ga)
> 
> # The following code shows how to obtain graphs using the 
> # ggplot2 plotting system 
> ## Not run: 
> ##D GA <- ga(type = "real-valued", 
> ##D          fitness = function(x) -(abs(x)+cos(x)), 
> ##D          lower = -20, upper = 20, 
> ##D          popSize = 20, pmutation = 0.2, maxiter = 50)
> ##D out <- plot(GA)
> ##D library(reshape2)
> ##D df <- melt(out[,c(1:3,5)], id.var = "iter")
> ##D library(ggplot2)
> ##D ggplot(out) +
> ##D   geom_ribbon(aes(x = iter, ymin = median, ymax = max, 
> ##D                   colour = "median", fill = "median")) +
> ##D   geom_line(aes(x = iter, y = max, colour = "max")) +
> ##D   geom_point(aes(x = iter, y = max, colour = "max")) +
> ##D   geom_line(aes(x = iter, y = mean, colour = "mean"), lty = 2) +
> ##D   geom_point(aes(x = iter, y = mean, colour = "mean"), pch = 1) +
> ##D   xlab("Generation") +  ylab("Fitness values") +
> ##D   scale_colour_manual(breaks = c("max", "mean", "median"),
> ##D                       values = c("green3", "dodgerblue3", adjustcolor("green3", alpha.f = 0.1))) +
> ##D   scale_fill_manual(breaks = "median",
> ##D                     values = adjustcolor("green3", alpha.f = 0.1)) +
> ##D   guides(fill = "none", 
> ##D          colour = guide_legend(override.aes = 
> ##D                                  list(fill = c(NA, NA, adjustcolor("green3", alpha.f = 0.1)),
> ##D                                        pch = c(19,1,NA)))) +
> ##D   theme_bw() +
> ##D   theme(legend.title = element_blank(), 
> ##D         legend.pos = "top",
> ##D         legend.background = element_blank())
> ## End(Not run)
> 
> 
> 
> cleanEx()
> nameEx("plot.gaisl-method")
> ### * plot.gaisl-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.gaisl-method
> ### Title: Plot of Islands Genetic Algorithm search path
> ### Aliases: plot,gaisl-method plot.gaisl
> ### Keywords: methods hplot
> 
> ### ** Examples
> 
> # See examples in help(gaisl)
> 
> 
> 
> cleanEx()
> nameEx("summary.de-method")
> ### * summary.de-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.de-method
> ### Title: Summary for Differential Evolution
> ### Aliases: summary,de-method summary.de print.summary.de
> ### Keywords: optimize
> 
> ### ** Examples
> 
> f <- function(x) abs(x)+cos(x)
> DE <- de(fitness = function(x) -f(x), 
+          lower = -20, upper = 20, run = 50)
> out <- summary(DE)
> print(out)
── Differential Evolution ────────────── 

DE settings: 
Type                  =  real-valued 
Population size       =  10 
Number of generations =  100 
Elitism               =  0 
Stepsize              =  0.8 
Crossover probability =  0.5 
Mutation probability  =  0 
Search domain = 
       x1
lower -20
upper  20

DE results: 
Iterations             = 90 
Fitness function value = -1 
Solution = 
              x1
[1,] 5.55337e-17
> str(out)
List of 12
 $ type       : chr "real-valued"
 $ popSize    : num 10
 $ maxiter    : num 100
 $ elitism    : int 0
 $ stepsize   : num 0.8
 $ pcrossover : num 0.5
 $ pmutation  : num 0
 $ domain     : num [1:2, 1] -20 20
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "lower" "upper"
  .. ..$ : chr "x1"
 $ suggestions: NULL
 $ iter       : int 90
 $ fitness    : num -1
 $ solution   : num [1, 1] 5.55e-17
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr "x1"
 - attr(*, "class")= chr "summary.de"
> 
> 
> 
> cleanEx()
> nameEx("summary.ga-method")
> ### * summary.ga-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.ga-method
> ### Title: Summary for Genetic Algorithms
> ### Aliases: summary,ga-method summary.ga print.summary.ga
> ### Keywords: optimize
> 
> ### ** Examples
> 
> f <- function(x)  abs(x)+cos(x)
> GA <- ga(type = "real-valued", 
+          fitness = function(x) -f(x), 
+          lower = -20, upper = 20, run = 50)
> out <- summary(GA)
> print(out)
── Genetic Algorithm ─────────────────── 

GA settings: 
Type                  =  real-valued 
Population size       =  50 
Number of generations =  100 
Elitism               =  2 
Crossover probability =  0.8 
Mutation probability  =  0.1 
Search domain = 
       x1
lower -20
upper  20

GA results: 
Iterations             = 100 
Fitness function value = -1.000022 
Solution = 
               x1
[1,] 2.239645e-05
> str(out)
List of 11
 $ type       : chr "real-valued"
 $ popSize    : num 50
 $ maxiter    : num 100
 $ elitism    : int 2
 $ pcrossover : num 0.8
 $ pmutation  : num 0.1
 $ domain     : num [1:2, 1] -20 20
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "lower" "upper"
  .. ..$ : chr "x1"
 $ suggestions: NULL
 $ iter       : int 100
 $ fitness    : num -1
 $ solution   : num [1, 1] 2.24e-05
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr "x1"
 - attr(*, "class")= chr "summary.ga"
> 
> 
> 
> cleanEx()
> nameEx("summary.gaisl-method")
> ### * summary.gaisl-method
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.gaisl-method
> ### Title: Summary for Islands Genetic Algorithms
> ### Aliases: summary,gaisl-method summary.gaisl print.summary.gaisl
> ### Keywords: optimize
> 
> ### ** Examples
> 
> ## Not run: 
> ##D f <- function(x)  abs(x)+cos(x)
> ##D GA <- gaisl(type = "real-valued", 
> ##D             fitness = function(x) -f(x), 
> ##D             lower = -20, upper = 20, run = 10,
> ##D             numIslands = 4)
> ##D out <- summary(GA)
> ##D print(out)
> ##D str(out)
> ## End(Not run)
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  6.664 0.282 14.635 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')