# malschains

##### Perform optimization with the MA-LS-Chains algorithm

This is the main function of the package. It minimizes the output of the function fn (for maximization, change the sign of the output of fn).

##### Usage

```
malschains(fn, lower, upper, dim, maxEvals = 10 * control$istep,
verbosity = 2, initialpop = NULL, control = malschains.control(),
seed = NULL, env)
```

##### Arguments

- fn
The function to minimize.

- lower
The lower bound (or bounds) of the search domain.

- upper
The upper bound (or bounds) of the search domain.

- dim
The dimension of the problem (if

`lower`

and`upper`

are vectors it is not needed).- maxEvals
The maximal number of evaluations of the fitness function.

- verbosity
Set the verbosity level. Currently, meaningful values are 0, 1, 2

- initialpop
An initial population for the evolutionary algorithm can be submitted (as a matrix). Here, prior knowledge can be introduced to get better results from the algorithm.

- control
A list containing the main options of the algorithm. See

`malschains.control`

.- seed
A seed value for the random number generator.

- env
The environment in which to evaluate the fitness function. If not given, it is generated.

##### Details

The output of the function when run with `verbosity=2`

is the following:

`EA::PopFitness`

The fitness of the best, the one at the 1st quartile, the one at the 3rd quartile, and the worst individual.`EA::Improvement`

Improvement of the individuals at the according ranked positions in the population (best, 1st quartile, 3rd quartile, worst).`LS`

The number of the individual which is improved on (in braces), its fitness before and after application of the LS procedure, and their difference.`EABest`

If the best fitness present in the population changed: same as`LS`

.

##### Value

the function returns a list containing the best individual, `sol`

, and its `fitness`

.
Furthermore, it contains some information on the optimization process, which can be seen using `print.malschains`

.

##### References

Molina, D., Lozano, M., S<U+00E1>nchez, A.M., Herrera, F. Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains (2011) Soft Computing, 15 (11), pp. 2201-2220.

Molina, D., Lozano, M., Garc<U+00ED>a-Mart<U+00ED>nez, C., Herrera, F. Memetic algorithms for continuous optimisation based on local search chains (2010) Evolutionary Computation, 18 (1), pp. 27-63.

*Documentation reproduced from package Rmalschains, version 0.2-6, License: GPL-3 | file LICENSE*