RDocumentation: map

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install.packages("GA", repos = "http://cran.us.r-project.org",
                 type = "source", dependencies = TRUE)

if(!require('purrr')) {
    install.packages('purrr')
    library('purrr')
}

1:10 %>%
  map(rnorm, n = 10) %>%
  map_dbl(mean)

# Or use an anonymous function
1:10 %>%
  map(function(x) rnorm(10, x))

# Or a formula
1:10 %>%
  map(~ rnorm(10, .x))

# Extract by name or position
# .default specifies value for elements that are missing or NULL
l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))
l1 %>% map("a", .default = "???")
l1 %>% map_int("b", .default = NA)
l1 %>% map_int(2, .default = NA)

# Supply multiple values to index deeply into a list
l2 <- list(
  list(num = 1:3,     letters[1:3]),
  list(num = 101:103, letters[4:6]),
  list()
)
l2 %>% map(c(2, 2))

# Use a list to build an extractor that mixes numeric indices and names,
# and .default to provide a default value if the element does not exist
l2 %>% map(list("num", 3))
l2 %>% map_int(list("num", 3), .default = NA)

# A more realistic example: split a data frame into pieces, fit a
# model to each piece, summarise and extract R^2
mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map(summary) %>%
  map_dbl("r.squared")

# Use map_lgl(), map_dbl(), etc to reduce to a vector.
# * list
mtcars %>% map(sum)
# * vector
mtcars %>% map_dbl(sum)

# If each element of the output is a data frame, use
# map_dfr to row-bind them together:
mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))
# (if you also want to preserve the variable names see
# the broom package)

# Supply multiple values to index deeply into a list
l2 <- list(
  list(num = 1:3,     letters[1:3]),
  list(num = 101:103, letters[4:6]),
  list()
)
l2[2]

l2 %>% map(c(2, 2))

formula <- "y ~ b0"
for(i in 1:100) {
  formula <- paste(formula, "+ b", i, "* x", i, sep="")
}
formula

library(GA)

library(GA)

# Definir función de fitness
fitness <- function(x) {
  # Evaluar polinomio en valores de x
  fo <- sum(x * c(0.2706, 0.3456, 0.1121, 0.2706))
  
  # Retornar valor objetivo (negativo del polinomio, ya que se busca maximizar)
  return(-fo)
}


# Definir límites para los valores de x
lower <- rep(0, 4)
upper <- rep(25, 4)

# Ejecutar algoritmo genético para optimizar la función de fitness
resultados <- ga(type = "real-valued", fitness = fitness, lower = lower, upper = upper, maxiter = 100)
summary(resultados)

library(GA)

# Definir función de fitness
fitness <- function(x) {
  # Evaluar polinomio en valores de x
  y <- b0 + sum(x * b[1:100])
  
  # Retornar valor objetivo (negativo del polinomio, ya que se busca maximizar)
  return(-y)
}

# Definir coeficientes del polinomio
b0 <- 1
b <- runif(100, -1, 1)  # coeficientes aleatorios entre -1 y 1

# Definir límites para los valores de x
lower <- rep(-10, 100)
upper <- rep(10, 100)

# Ejecutar algoritmo genético para optimizar la función de fitness
resultados <- ga(type = "real-valued", fitness = fitness, lower = lower, upper = upper, maxiter = 100)
summary(resultados)