asemoa {ecr} | R Documentation |
Implementation of the NSGA-II EMOA algorithm by Deb.
Description
The AS-EMOA, short for aspiration set evolutionary multi-objective
algorithm aims to incorporate expert knowledge into multi-objective 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 coordinate-wise 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 AS-EMOA 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: 153-156.
[2] G. Rudolph, O. Schuetze, C. Grimme, and H. Trautmann: A Multiobjective
Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration
Sets, pp. 261-273 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]