REMEDA: Random Embedding EDA for Optimising Functions with Intrinsic Dimension

Sanyang, Momodou L.; Kabán, Ata

doi:10.1007/978-3-319-45823-6_80

Cited by 12 publications

(26 citation statements)

References 13 publications

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“…Beside building an explicit model of the objective function, as is the case with meta-modeling, approximation can be built into problem representation [84] or be achieved by means of dimensionality reduction through transformation [85][86][87][88][89][90][91] or finding intrinsic dimensions of a problem [92]. Wang et al [93] combined the benefit of meta-models and dimensionality reduction by using auto-encoders to find lower dimensional features of graph embedding problems and use them to construct a surrogate model to approximate the robustness value of large-scale graph networks.…”

Section: Approximation and Surrogate Modelingmentioning

confidence: 99%

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Omidvar

Yao

2022

IEEE Trans. Evol. Computat.

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This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

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Section: Approximation and Surrogate Modelingmentioning

confidence: 99%

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Omidvar

Yao

2022

IEEE Trans. Evol. Computat.

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“…However, no technique has been tailored for sparse problems before. It is noteworthy that LOPs with a low intrinsic dimensionality [38] are similar to sparse LOPs to some extent, but the optimal solutions of these two types of problems are significantly different. For sparse LOPs, only a small portion of decision variables contribute to the optimal solutions, and the noncontributing decision variables should be set to 0.…”

Section: A Sparse Mops In Large-scale Optimization Problemsmentioning

confidence: 99%

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

Tian

Zhang

Wang

et al. 2020

IEEE Trans. Evol. Computat.

266

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In the last two decades, a variety of different types of multi-objective optimization problems (MOPs) have been extensively investigated in the evolutionary computation community. However, most existing evolutionary algorithms encounter difficulties in dealing with MOPs whose Pareto optimal solutions are sparse (i.e., most decision variables of the optimal solutions are zero), especially when the number of decision variables is large. Such large-scale sparse MOPs exist in a wide range of applications, for example, feature selection that aims to find a small subset of features from a large number of candidate features, or structure optimization of neural networks whose connections are sparse to alleviate overfitting. This paper proposes an evolutionary algorithm for solving large-scale sparse MOPs. The proposed algorithm suggests a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration, to ensure the sparsity of the generated solutions. Moreover, this paper also designs a test suite to assess the performance of the proposed algorithm for large-scale sparse MOPs. Experimental results on the proposed test suite and four application examples demonstrate the superiority of the proposed algorithm over seven existing algorithms in solving large-scale sparse MOPs.

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“…MA-SW-Chain [15] is a hybrid algorithm that as- In doing so, the method will mitigate the curse of dimensionality. A different way to exploit hidden low dimensional structure was proposed in [17], suggesting that a notion of intrinsic dimension of a function can replace the ambient dimension of the inputs to the function, provided that the function has a special structure.…”

Section: Related Work In Large-scale Black-box Optimisationmentioning

confidence: 99%

Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles

Sanyang

Kabán

2019

J. Comput. Sci. Technol.

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REMEDA: Random Embedding EDA for Optimising Functions with Intrinsic Dimension

Cited by 12 publications

References 13 publications

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

Large-Scale Estimation of Distribution Algorithms with Adaptive Heavy Tailed Random Projection Ensembles

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