asemoa {ecr}  R Documentation 
Implementation of the NSGAII EMOA algorithm by Deb.
Description
The ASEMOA, short for aspiration set evolutionary multiobjective
algorithm aims to incorporate expert knowledge into multiobjective optimization [1].
The algorithm expects an aspiration set, i.e., a set of reference points. It
then creates an approximation of the pareto front close to the aspiration set
utilizing the average Hausdorff distance.
Usage
asemoa(fitness.fun, n.objectives = NULL, minimize = NULL, n.dim = NULL,
lower = NULL, upper = NULL, mu = 10L, aspiration.set = NULL,
normalize.fun = NULL, dist.fun = ecr:::computeEuclideanDistance, p = 1,
parent.selector = setup(selSimple), mutator = setup(mutPolynomial, eta =
25, p = 0.2, lower = lower, upper = upper), recombinator = setup(recSBX, eta
= 15, p = 0.7, lower = lower, upper = upper),
terminators = list(stopOnIters(100L)))
Arguments
fitness.fun 
[function ]
The fitness function.

n.objectives 
[integer(1) ]
Number of objectives of obj.fun .
Optional if obj.fun is a benchmark function from package smoof.

minimize 
[logical(n.objectives) ]
Logical vector with ith entry TRUE if the ith objective of fitness.fun
shall be minimized. If a single logical is passed, it is assumed to be valid
for each objective.

n.dim 
[integer(1) ]
Dimension of the decision space.

lower 
[numeric ]
Vector of minimal values for each parameter of the decision space in case
of float or permutation encoding.
Optional if obj.fun is a benchmark function from package smoof.

upper 
[numeric ]
Vector of maximal values for each parameter of the decision space in case
of float or permutation encoding.
Optional if obj.fun is a benchmark function from package smoof.

mu 
[integer(1) ]
Population size. Default is 10.

aspiration.set 
[matrix ]
The aspiration set. Each column contains one point of the set.

normalize.fun 
[function ]
Function used to normalize fitness values of the individuals
before computation of the average Hausdorff distance.
The function must have the formal arguments “set” and “aspiration.set”.
Default is NULL , i.e., no normalization at all.

dist.fun 
[function ]
Distance function used internally by Hausdorff metric to compute distance
between two points. Expects a single vector of coordinatewise differences
between points.
Default is computeEuclideanDistance .

p 
[numeric(1) ]
Parameter p for the average Hausdorff metric. Default is 1.

parent.selector 
[ecr_selector ]
Selection operator which implements a procedure to copy individuals from a
given population to the mating pool, i. e., allow them to become parents.

mutator 
[ecr_mutator ]
Mutation operator of type ecr_mutator .

recombinator 
[ecr_recombinator ]
Recombination operator of type ecr_recombinator .

terminators 
[list ]
List of stopping conditions of type “ecr_terminator”.
Default is to stop after 100 iterations.

Value
[ecr_multi_objective_result
]
Note
This is a pure R implementation of the ASEMOA algorithm. It hides the regular
ecr interface and offers a more R like interface while still being quite
adaptable.
References
[1] Rudolph, G., Schuetze, S., Grimme, C., Trautmann, H: An Aspiration Set
EMOA Based on Averaged Hausdorff Distances. LION 2014: 153156.
[2] G. Rudolph, O. Schuetze, C. Grimme, and H. Trautmann: A Multiobjective
Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration
Sets, pp. 261273 in A.A. Tantar et al. (eds.): Proceedings of EVOLVE  A
bridge between Probability, Set Oriented Numerics and Evolutionary Computation
V, Springer: Berlin Heidelberg 2014.
[Package
ecr version 2.1.0
Index]