Multi-objective NM-Landscapes

Santana, Roberto; Mendiburu, Alexander; Lozano, José A.

doi:10.1145/2739482.2764704

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…There are several problem features that define the structure of fitness landscapes and can influence the difficulty level during the search. In this paper we are interested in the MNKlandscape problem, which has been explored by [9,10], for example, in order to study algorithm's behavior with respect to a set of relevant problem features. The features examined in our work are based on [11] and will be presented in Table II of Section IV.…”

Section: B Fitness Landscapementioning

confidence: 99%

“…They investigated some features like the distribution of local and Pareto optima, fitness distance correlation, ruggedness and random walk on the multi-objective generalized assignment problem. This paper addresses a combinatorial MOP -multi-objective NK-landscape (MNK) model which has been recently explored in other works in the literature [9,10,11]. In particular, EDAs that use different types of probabilistic models, including Bayesian Networks, have already been applied to the monoobjective NK problem [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…This paper addresses a combinatorial MOP -multi-objective NK-landscape (MNK) model which has been recently explored in other works in the literature [9,10,11]. In particular, EDAs that use different types of probabilistic models, including Bayesian Networks, have already been applied to the monoobjective NK problem [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Performance of Multi-Objective Estimation of Distribution Algorithms for Combinatorial Problems

Martins

Yafrani

Santana

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-landscape problem for 2, 3, 5 and 8 objectives. We also compare the results of mBOA with those provided by NSGA-III through the analysis of their estimated runtime necessary to identify an approximation of the Pareto front. Moreover, in order to scrutinize the probabilistic graphic model obtained by mBOA, the Pareto front is examined according to a probabilistic view. The fitness landscape study shows that mBOA is moderately or loosely influenced by some problem features, according to a simple and a multiple linear regression model, which is being proposed to predict the algorithms performance in terms of the estimated runtime. Besides, we conclude that the analysis of the probabilistic graphic model produced at the end of evolution can be useful to understand the convergence and diversity performances of the proposed approach.

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Section: B Fitness Landscapementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Performance of Multi-Objective Estimation of Distribution Algorithms for Combinatorial Problems

Martins

Yafrani

Santana

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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“…The multi-objective NM-landscape model (mNM-landscape) is defined [20] as a vector function mapping binary vectors of solutions into m real numbers f(.) = (f 1 (.…”

Section: Multi-objective Nm-landscapesmentioning

confidence: 99%

“…In [20], the NM-landscape was extended to multi-objective problems and used to study the influence of the parameters in the characteristics of the MOP. We build on the work presented in [20] to propose the use of the multi-objective NM-landscape (mNMlandscape) for investigating how the patterns of interactions in the landscape model influence the shape of the Pareto front. We go one step further and propose the use of factorized approximations computed from the landscapes to approximate the Pareto fronts.…”

Section: Introductionmentioning

confidence: 99%

Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions

Santana,

Mendiburu,

Lozano

2015

Preprint

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NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal M . The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we propose the combination of the multi-objective NM-landscape model and the Boltzmann distribution to obtain Pareto-front approximations. We investigate the joint effect of the parameters of the NM-landscapes and the probabilistic factorizations in the shape of the Pareto front approximations.

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Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape

et al. 2021

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Multi-objective NM-Landscapes

Cited by 3 publications

References 4 publications

On the Performance of Multi-Objective Estimation of Distribution Algorithms for Combinatorial Problems

On the Performance of Multi-Objective Estimation of Distribution Algorithms for Combinatorial Problems

Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions

Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape

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