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.
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 = 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))
)
[ecr_multi_objective_result
]
[function
]
The fitness function.
[integer(1)
]
Number of objectives of obj.fun
.
Optional if obj.fun
is a benchmark function from package smoof.
[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.
[integer(1)
]
Dimension of the decision space.
[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.
[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.
[integer(1)
]
Population size. Default is 10.
[matrix
]
The aspiration set. Each column contains one point of the set.
[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.
[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
.
[numeric(1)
]
Parameter \(p\) for the average Hausdorff metric. Default is 1.
[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.
[ecr_mutator
]
Mutation operator of type ecr_mutator
.
[ecr_recombinator
]
Recombination operator of type ecr_recombinator
.
[list
]
List of stopping conditions of type “ecr_terminator”.
Default is to stop after 100 iterations.
[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.