# Explicit exploration in evolutionary algorithms It makes an explicit exploration as proposed in Salinas-Gutiérrez and Zavala, 2023. ```r ExplicitExploration( fun, lower, upper, n = 30, maxiter, k = 5, tolerance = 0.01, ... ) ``` ## Arguments - `fun`: A function to be minimized, with first argument the vector of parameters over which minimization is to take place. - `lower`: Lower bounds on the parameters. - `upper`: Upper bounds on the parameters. - `n`: Number of individuals per generation - `maxiter`: Maximum number of iterations. - `k`: Number of consecutive generations without change in distribution before stopping. - `tolerance`: Criterion for determining whether the distribution has changed. - `...`: Additional arguments of the objective function. ## Returns Returns a list with the following entries: - **`par`**: The top n individuals from the entire search. - **`Y`**: The value of the objective function for each of the best individuals. - **`n_gen`**: Number of generations required for the search. - **`par_historical`**: All individuals generated during the search. - **`historical`**: The value of the objective function for all generated individuals. ## Examples ```r fun <- function(X){ D <- length(X) f <- abs(sum(X^2) - D)^(1/4) + (0.5 * sum(X^2) + sum(X))/D + 0.5 return(f) } n <- 30 k <- 2 tolerance <- 0.01 lower <- c(-5,-5) upper <- c(5,5) res <- ExplicitExploration(fun, lower = lower, upper = upper,n = n, maxiter = 20, k = k) z <- outer(X = seq(-5, 5, 0.05), Y = seq(-5, 5, 0.05), FUN = Vectorize(function(X, Y) { fun(c(X, Y)) })) contour(seq(-5, 5, 0.05),seq(-5, 5, 0.05),z, nlevels = 20, cex.axis = .8) points(res$par_historical[,1],res$par_historical[,2], col = "blue") points(res$par[,1],res$par[,2], col = "red", pch = 19) ``` ## References Salinas-Gutiérrez, R., & Zavala, A. E. M. (2023).An explicit exploration strategy for evolutionary algorithms. Applied Soft Computing, 140. https://doi.org/10.1016/j.asoc.2023.110230

ExplicitExploration function