Model-based methods for continuous and discrete global optimization
T Bartz-Beielstein, M Zaefferer - Applied Soft Computing, 2017 - Elsevier
The use of surrogate models is a standard method for dealing with complex real-world
optimization problems. The first surrogate models were applied to continuous optimization …
optimization problems. The first surrogate models were applied to continuous optimization …
A distance-based ranking model estimation of distribution algorithm for the flowshop scheduling problem
The aim of this paper is two-fold. First, we introduce a novel general estimation of distribution
algorithm to deal with permutation-based optimization problems. The algorithm is based on …
algorithm to deal with permutation-based optimization problems. The algorithm is based on …
A review of distances for the Mallows and Generalized Mallows estimation of distribution algorithms
The Mallows (MM) and the Generalized Mallows (GMM) probability models have
demonstrated their validity in the framework of Estimation of distribution algorithms (EDAs) …
demonstrated their validity in the framework of Estimation of distribution algorithms (EDAs) …
Properties of the mallows model depending on the number of alternatives: a warning for an experimentalist
N Boehmer, P Faliszewski… - … Conference on Machine …, 2023 - proceedings.mlr.press
The Mallows model is a popular distribution for ranked data. We empirically and theoretically
analyze how the properties of rankings sampled from the Mallows model change when …
analyze how the properties of rankings sampled from the Mallows model change when …
PerMallows: An R package for Mallows and generalized Mallows models
In this paper we present the R package PerMallows, which is a complete toolbox to work
with permutations, distances and some of the most popular probability models for …
with permutations, distances and some of the most popular probability models for …
Low-dimensional euclidean embedding for visualization of search spaces in combinatorial optimization
K Michalak - Proceedings of the Genetic and Evolutionary …, 2019 - dl.acm.org
This abstract summarizes the results reported in the paper [3]. In this paper a method named
Low-Dimensional Euclidean Embedding (LDEE) is proposed, which can be used for …
Low-Dimensional Euclidean Embedding (LDEE) is proposed, which can be used for …
Sampling and learning Mallows and Generalized Mallows models under the Cayley distance
Abstract The Mallows and Generalized Mallows models are compact yet powerful and
natural ways of representing a probability distribution over the space of permutations. In this …
natural ways of representing a probability distribution over the space of permutations. In this …
Multiobjective decomposition-based mallows models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem
Estimation of distribution algorithms (EDAs) have become a reliable alternative to solve a
broad range of single and multi-objective optimization problems. Recently, distance-based …
broad range of single and multi-objective optimization problems. Recently, distance-based …
Bayesian optimization for parameter tuning in evolutionary algorithms
Advances in evolutionary computation have demonstrated that Evolutionary Algorithms
(EAs) proposed in this area are a solid alternative for solving combinatorial and continuous …
(EAs) proposed in this area are a solid alternative for solving combinatorial and continuous …
The Plackett-Luce ranking model on permutation-based optimization problems
J Ceberio, A Mendiburu… - 2013 IEEE congress on …, 2013 - ieeexplore.ieee.org
Estimation of distribution algorithms are known as powerful evolutionary algorithms that
have been widely used for diverse types of problems. However, they have not been …
have been widely used for diverse types of problems. However, they have not been …