Multi-Objective Evolutionary Instance Selection for Regression Tasks

Kordos, Mirosław; Łapa, Krystian

doi:10.3390/e20100746

Cited by 15 publications

(7 citation statements)

References 66 publications

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“…As the use of multi-parent crossover operators can significantly accelerate (up to three times) the convergence speed of the classical genetic algorithms [30,31] (as well their single-objective as multi-objective version built upon the NSGA-II algorithm [32]), one of the ideas of this work was to apply the multi-parent approach to the crossover operators in the route and product placement optimization problems in hope that it can provide better results.…”

Section: Multi-parent Crossover Operatorsmentioning

confidence: 99%

Optimization of Warehouse Operations with Genetic Algorithms

et al. 2020

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We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.

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Section: Multi-parent Crossover Operatorsmentioning

confidence: 99%

Optimization of Warehouse Operations with Genetic Algorithms

et al. 2020

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“…The next three allow us to compare the way the FSM11 algorithm works with other PBAs (see Tables 10-12). Welch's test and Wilcoxon signed-rank test are the most widely used statistical tests for determining if the difference between the results of two models is non-random; Fisher-Snedecor test compares the convergence of two independent measurement series [50].…”

Section: A Assumptions Adopted In the Simulationsmentioning

confidence: 99%

Algorithm Based on Population With a Flexible Search Mechanism

2019

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In this paper, we propose a new population-based algorithm (PBA) that adjusts its search mechanism to the problem which is to be optimized. It uses a set of search operators used in commonly-used PBAs and this selection can be made freely. The proposed algorithm selects two operators from this set, i.e. one for exploration and the other for exploitation of the search space of the solution. This approach can be an alternative for finding new inspirations for the development of PBAs and for modifying the existing PBAs. The effectiveness of the proposed algorithm has been tested using typical benchmarks for testing PBAs. An interesting aspect of the performed simulations is the analysis of how operators are used during the optimization process.

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“…Besides that, some novel reconstructed algorithms were proposed for enforcement of the sparsity and the structure of the reconstructed source by combining ℓ 1 and total variation, or utilizing Gaussian Markov random field [8,21]. Apart from that, in the sparse optimization research of bearings vibration signals and long term health monitoring data, the optimized Lasso and the Least Square QR-factorization (Lasso-LSQR) takes the Basis Pursuit and ℓ 1 regularization with the advantage of fast iteration and accurate sparse reconstruction [26][27][28][29][30]. It proposes a new inspiration for XLCT in study of reconstruction algorithm.…”

Section: Introductionmentioning

confidence: 99%

X‐ray luminescence computed tomography using a hybrid proton propagation model and Lasso‐LSQR algorithm

Guo

Zhao

et al. 2021

Journal of Biophotonics

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X‐ray luminescence computed tomography (XLCT) uses external X‐rays for luminescence excitation, which is becoming a promising molecular imaging technique with superb penetration depth and spatial resolution. To achieve the tomographic mapping of luminescence distribution, accurate optical propagation model and suitable reconstruction method are two keys for XLCT, but not satisfied. To overcome the limitation of the single proton propagation model (e.g., DE, SP3), we adopted a hybrid diffusion equation with third order simplified spherical harmonics (DE‐SP3) model for XLCT. To enable fast iteration and accurate sparse reconstruction, we also integrated in the inversion optimization, with a novel Least Square QR‐factorization based on the Lasso (Lasso‐LSQR) algorithm. We first simulated the light propagation in various kinds of organs under DE model and SP3 model, respectively. By comparison with the Monte Carlo, these tissues can be categorized into two types, namely DE‐fitted tissues that include muscle and lung, and SP3‐fitted tissues including heart, kidney, liver, and stomach. According to the above classification results, we built a hybrid DE‐SP3 model to more accurately describing light transport. Numerical simulations and in vivo experiments illustrated that hybrid DE‐SP3 model achieves superior reconstruction performance in terms of location accuracy, and spatial resolution than DE, and less computational cost than SP3. The hybrid DE‐SP3 model materializes a balance between accuracy and efficiency for XLCT.

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Multi-Objective Evolutionary Instance Selection for Regression Tasks

Cited by 15 publications

References 66 publications

Optimization of Warehouse Operations with Genetic Algorithms

Optimization of Warehouse Operations with Genetic Algorithms

Algorithm Based on Population With a Flexible Search Mechanism

X‐ray luminescence computed tomography using a hybrid proton propagation model and Lasso‐LSQR algorithm

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