Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme

Luong, Ngoc Hoang; Poutré, Han La; Bosman, Peter A. N.

doi:10.1016/j.swevo.2018.02.005

Cited by 33 publications

(34 citation statements)

References 28 publications

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“…GOMEA has its roots in the Linkage Tree Genetic Algorithm (LTGA) (Thierens, 2010) for binary variables, which was later generalized and renamed (Thierens and Bosman, 2011). Subsequent efficiency enhancements, restart mechanisms, and multi-objective extensions, adapted GOMEA to its current state Luong et al, 2018b), which is considered to be among the state-of-the-art for discrete optimization.…”

Section: Gene-pool Optimal Mixingmentioning

confidence: 99%

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

Bouter

Alderliesten

Bosman

2021

Evolutionary Computation

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It is known that to achieve efficient scalability of an Evolutionary Algorithm (EA), dependencies (also known as linkage) must be properly taken into account during variation. In a Gray-Box Optimization (GBO) setting, exploiting prior knowledge regarding these dependencies can greatly benefit optimization. We specifically consider the setting where partial evaluations are possible, meaning that the partial modification of a solution can be efficiently evaluated. Such problems are potentially very difficult, e.g., non-separable, multi-modal, and multi-objective. The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) can effectively exploit partial evaluations, leading to a substantial improvement in performance and scalability. GOMEA was recently shown to be extendable to real-valued optimization through a combination with the real-valued estimation of distribution algorithm AMaLGaM. In this article, we definitively introduce the Real-Valued GOMEA (RV-GOMEA), and introduce a new variant, constructed by combining GOMEA with what is arguably the best-known real-valued EA, the Covariance Matrix Adaptation Evolution Strategies (CMA-ES). Both variants of GOMEA are compared to L-BFGS and the Limited Memory CMA-ES (LM-CMAES). We show that both variants of RV-GOMEA achieve excellent performance and scalability in a GBO setting, which can be orders of magnitude better than that of EAs unable to efficiently exploit the GBO setting.

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Section: Gene-pool Optimal Mixingmentioning

confidence: 99%

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

Bouter

Alderliesten

Bosman

2021

Evolutionary Computation

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“…The Interleaved Multi-start Scheme (IMS [28,29]) is employed to operate multiple populations of increasing sizes asynchronously. For every b = 8 generations of population P i , the subsequent population P i+1 (with |P i+1 | = 2 × |P i |) is run for one generation.…”

Section: Mo-rv-gomeamentioning

confidence: 99%

“…The generation bases of IMS b = 2, and 4 have been suggested for the discrete optimization cases [29] while b = 8 was found to give good results for MO-RV-GOMEA (i.e., for realvalued optimization) [17]. Note that while each population is run separately, they contribute to the same elitist archive.…”

Section: A6 Interleaved Multi-start Scheme (Ims)mentioning

confidence: 99%

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

Luong

Alderliesten

Pieters

et al. 2019

Applied Soft Computing

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h i g h l i g h t s• Challenges of the brachytherapy planning problem for prostate cancer treatment.• Accuracy and efficiency issues in multi-objective prostate brachytherapy planning. • A multi-resolution scheme to accelerate the optimization of brachytherapy planning. • A multi-core CPU parallelization of our approach in brachytherapy planning. • Comparing solutions created by our approach with clinically approved treatment plans. a b s t r a c tPurpose: Prostate high-dose-rate brachytherapy (HDR-BT) planning involves determining the movement that a high-strength radiation stepping source travels through the patient's body, such that the resulting radiation dose distribution sufficiently covers tumor volumes and safely spares nearby healthy organs from radiation risks. The Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) has been shown to be able to effectively handle this inherent bi-objective nature of HDR-BT planning. However, in clinical practice there is a very restricted planning time budget (often less than 1 h) for HDR-BT planning, and a considerable amount of running time needs to be spent before MO-RV-GOMEA finds a good trade-off front of treatment plans (about 20-30 min on a single CPU core) with sufficiently accurate dose calculations, limiting the applicability of the approach in the clinic. To address this limitation, we propose an efficiency enhancement technique for MO-RV-GOMEA solving the bi-objective prostate HDR-BT planning problem. Methods: Dose-Volume (DV) indices are often used to assess the quality of HDR-BT plans. The accuracy of these indices depends on the number of dose calculation points at which radiation doses are computed. These are randomly uniformly sampled inside target volumes and organs at risk. In available HDR-BT planning optimization algorithms, the number of dose calculation points is fixed. The more points are used, the better the accuracy of the obtained results will be, but also the longer the algorithms need to be run. In this work, we introduce a so-called multi-resolution scheme that gradually increases the number of dose calculation points during the optimization run such that the running time can be substantially reduced without compromising on the accuracy of the obtained results. Results and conclusion: Experiments on a data set of 18 patient cases show that with the multiresolution scheme, MO-RV-GOMEA can achieve a sufficiently good trade-off front of treatment plans after five minutes of running time on a single CPU core (4-6 times faster than the old approach with a fixed number of dose calculation points). When the optimization with the multi-resolution scheme is run on a quad-core machine, five minutes are enough to obtain trade-off fronts that are nearly as good as those obtained by running optimization with the old approach in one hour (i.e., 12 times faster). This leaves ample time to perform the selection of the preferred treatment plan from the trade-off front for the specific patient at hand. Furthermore, compa...

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“…One of the key advantages of MOEA/D when compared to NSGA-II is that it does not require the computation of so-called crowding distance that is computationally expensive. NSGA-II and MOEA/D are typical reference methods in multi-objective optimisation [19], so they are also employed as the baseline in this paper.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective parameter-less population pyramid for solving industrial process planning problems

Przewoźniczek

Dziurzański

Zhao

et al. 2021

Swarm and Evolutionary Computation

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Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the state-of-the-art methods that employ linkage learning is the Parameter-less Population Pyramid (P3). P3 is dedicated to solving singleobjective problems in discrete domains. Recent research shows that P3 is highly competitive when addressing problems with so-called overlapping blocks, which are typical for practical problems. In this paper, we consider a multi-objective industrial process planning problem that arises from practice and is NP-hard.To handle it, we propose a multi-objective version of P3. The extensive research shows that our proposition outperforms the competing methods for the considered practical problem and typical multi-objective benchmarks.

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Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme

Cited by 33 publications

References 28 publications

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

Multi-Objective parameter-less population pyramid for solving industrial process planning problems

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