b6e6rl: Adaptive differential evolution, b6e6rl algorithm
In b6e6rl: Adaptive differential evolution, b6e6rl variant

Description Usage Arguments Value Author(s) References Examples

This function serches for the global minimum using b6e6rl variant of adaptive differential evolution.

1	b6e6rl(fn_name, a, b, N, my_eps, max_evals, n0, delta)

`fn_name`	Name of function which minimum is to find
`a`	Vector of lower bounds of the search space (length=dimension of the search space)
`b`	Vector of upper bounds of the search space (length=dimension of the search space)
`N`	Size of population
`my_eps`	Small positive value, the algortihm stops when fmax-fmin < my_eps
`max_evals`	Maximum count of function evaluations per one dimension of the problem
`n0`	Input parameter controling the competition of the strategies, usualy n0=2
`delta`	Input parameter (critical probability), usualy delta=1/60

`x_star`	Aproximation of the global minimum point found by search (vector of length=d)
`fn_star`	Functional value at x_star
`func_evals`	Count of function evaluations
`success`	Count of succesfull generations of the trial point
`nrst`	Count of resets, when any probability value is less than delta
`cni`	Counts of succesful selection of each strategy (vector of length=12)

Marek Spruzina, University of Ostrava

Tvrdik, J. Adaptation in Differential Evolution: A Numerical Comparison. APPL SOFT COMPUT. 2009, Vol. 9, pp. 1149-1155.

Tvrdik, J. Self-adaptive Variants of Differential Evolution with Exponential Crossover. Analele Universitatii de Vest, Timisoara.Seria Matematica-Informatica.. 2009, Vol. 47, pp. 151- 168.

##Example of the b6e6rl call

fn_name <- ("f_dejong")
a <- c(-30,-30,-30)
b <- c(30,30,30)
N <- 60
max_evals <- 20000
my_eps <- 0.000001
n0 <- 2
delta <- 1/(5*12)

b6e6rl(fn_name, a, b, N, my_eps, max_evals, n0, delta)