CMAES {adagio}

Covariance Matrix Adaptation Evolution Strategy

Description

The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex optimization problems in continuous domain. The CMA-ES is typically applied to unconstrained or bounded constraint optimization problems, and search space dimensions between three and fifty.

Usage

pureCMAES(par, fun, lower = NULL, upper = NULL, sigma = 0.5,
                    stopfitness = -Inf, stopeval = 1000*length(par)^2, ...)

Arguments

par

objective variables initial point.

fun

objective/target/fitness function.

lower, upper

lower and upper bounds for the parameters.

sigma

coordinate wise standard deviation (step size).

stopfitness

stop if fitness < stopfitness (minimization).

stopeval

stop after stopeval number of function evaluations

...

additional parameters to be passed to the function.

Details

The CMA-ES implements a stochastic variable-metric method. In the very particular case of a convex-quadratic objective function the covariance matrix adapts to the inverse of the Hessian matrix, up to a scalar factor and small random fluctuations. The update equations for mean and covariance matrix maximize a likelihood while resembling an expectation-maximization algorithm.

Value

Returns a list with components xmin and fmin.

Be patient; for difficult problems or high dimensions the function may run for several minutes; avoid problem dimensions of 30 and more!

Note

There are other implementations of Hansen's CMAES in package ‘cmaes’ (simplified form) and in package ‘parma’ as cmaes() (extended form).

Author(s)

Copyright (c) 2003-2010 Nikolas Hansen for Matlab code PURECMAES; converted to R by Hans W Borchers. (Hansen's homepage: www.cmap.polytechnique.fr/~nikolaus.hansen/)

References

Hansen, N. (2011). The CMA Evolution Strategy: A Tutorial.
https://arxiv.org/abs/1604.00772

Hansen, N., D.V. Arnold, and A. Auger (2013). Evolution Strategies. J. Kacprzyk and W. Pedrycz (Eds.). Handbook of Computational Intelligence, Springer-Verlag, 2015.

See Also

cmaes::cmaes, parma::cmaes

Examples

## Not run: 
##  Polynomial minimax approximation of data points
##  (see the Remez algorithm)
n <- 10; m <- 101           # polynomial of degree 10; no. of data points
xi <- seq(-1, 1, length = m)
yi <- 1 / (1 + (5*xi)^2)    # Runge's function

pval <- function(p, x)      # Horner scheme
    outer(x, (length(p) - 1):0, "^") %*% p

pfit <- function(x, y, n)   # polynomial fitting of degree n
    qr.solve(outer(x, seq(n, 0), "^"), y)

fn1 <- function(p)           # objective function
    max(abs(pval(p, xi) - yi))

pf <- pfit(xi, yi, 10)      # start with a least-squares fitting
sol1 <- pureCMAES(pf, fn1, rep(-200, 11), rep(200, 11))
zapsmall(sol1$xmin)
# [1]  -50.24826    0.00000  135.85352    0.00000 -134.20107    0.00000
# [7]   59.19315    0.00000  -11.55888    0.00000    0.93453

print(sol1$fmin, digits = 10)
# [1] 0.06546780411

##  Polynomial fitting in the L1 norm
##  (or use LP or IRLS approaches)
fn2 <- function(p)
    sum(abs(pval(p, xi) - yi))

sol2 <- pureCMAES(pf, fn2, rep(-100, 11), rep(100, 11))
zapsmall(sol2$xmin)
# [1] -21.93238   0.00000  62.91083   0.00000 -67.84847   0.00000 
# [7]  34.14398   0.00000  -8.11899   0.00000   0.84533

print(sol2$fmin, digits = 10)
# [1] 3.061810639

## End(Not run)