A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

Yang, Peng; Tang, Ke; Yao, Xin

doi:10.1109/access.2019.2938765

Cited by 35 publications

(13 citation statements)

References 61 publications

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“…The proposed algorithm was tested on problems with up to 1000 dimensions. Yang et al [116] argue that decomposition-based methods, despite their modular nature, cannot be readily parallelized due to defects in how partial solutions are evaluated. They show that the objective function used to assign a fitness to a partial solution is not consistent with the ideal fitness assignment.…”

Section: Parallelizationmentioning

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: Parallelizationmentioning

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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“…Furthermore, as RL does not necessarily touch the domain-specific knowledge, the empirical results maybe more representative than testing on the concrete real-world problems. 2) EAs have been shown to be promising solutions to RL problems as the population-based nature of EAs not only provides the urgent exploration ability to RL [15], but also provides other merits such as parallel acceleration [25][26][27], noisy-resistance [28,29], and compatibility of training non-differentiable policies (e.g., trees [30]). Also notice that the canonical NES has been successfully applied to playing Atari games [15].…”

Section: Ncnes For Reinforcement Learningmentioning

confidence: 99%

Parallel exploration via negatively correlated search

Yang

Tang

et al. 2021

Front. Comput. Sci.

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Effective exploration is key to a successful search process. The recently proposed negatively correlated search (NCS) tries to achieve this by coordinated parallel exploration, where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously. Despite successful applications of NCS, the negatively correlated search behaviors were mostly devised by intuition, while deeper (e.g., mathematical) understanding is missing. In this paper, a more principled NCS, namely NCNES, is presented, showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic models. Reinforcement learning, for which exploration is of particular importance, are considered for empirical assessment. The proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari games. Empirical results show that the significant advantages of NCNES, especially on games with uncertain and delayed rewards, can be highly owed to the effective parallel exploration ability.

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“…Large scale multi-objective competitive swarm optimizer also has promise with a more efficient search algorithm and can be paired with decision variable analysis or grouping to reduce search space as well [66,76]. The current problem can naturally lend itself to divide and conquer into sub-samples which could be optimized in parallel thus benefitting from recent large-scale optimization swarms [70,78]. Given the computational expense of the problem can also benefit from application of recent distributed algorithms that offer the benefit of more efficient parallel search and more efficient particle updates [77].…”

Section: Improving Optimization Future Workmentioning

confidence: 99%

Optimal subset selection for causal inference using machine learning ensembles and particle swarm optimization

Sharma

Willy

Bischoff

2020

Complex Intell. Syst.

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We suggest and evaluate a method for optimal construction of synthetic treatment and control samples for the purpose of drawing causal inference. The balance optimization subset selection problem, which formulates minimization of aggregate imbalance in covariate distributions to reduce bias in data, is a new area of study in operations research. We investigate a novel metric, cross-validated area under the receiver operating characteristic curve (AUC) as a measure of balance between treatment and control groups. The proposed approach provides direct and automatic balancing of covariate distributions. In addition, the AUC-based approach is able to detect subtler distributional differences than existing measures, such as simple empirical mean/variance and count-based metrics. Thus, optimizing AUCs achieves a greater balance than the existing methods. Using 5 widely used real data sets and 7 synthetic data sets, we show that optimization of samples using existing methods (Chi-square, mean variance differences, Kolmogorov–Smirnov, and Mahalanobis) results in samples containing imbalance that is detectable using machine learning ensembles. We minimize covariate imbalance by minimizing the absolute value of the distance of the maximum cross-validated AUC on $$ M $$ M folds from 0.50, using evolutionary optimization. We demonstrate that particle swarm optimization (PSO) outperforms modified cuckoo swarm (MCS) for a gradient-free, non-linear noisy cost function. To compute AUCs, we use supervised binary classification approaches from the machine learning and credit scoring literature. Using superscore ensembles adds to the classifier-based two-sample testing literature. If the mean cross-validated AUC based on machine learning is 0.50, the two groups are indistinguishable and suitable for causal inference.

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A Parallel Divide-and-Conquer-Based Evolutionary Algorithm for Large-Scale Optimization

Cited by 35 publications

References 61 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

Parallel exploration via negatively correlated search

Optimal subset selection for causal inference using machine learning ensembles and particle swarm optimization

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