Two-edge graphical linkage model for DSMGA-II

Chen, Ping-Lin; Peng, Chun-Jen; Chang-yi, LU; Yu, Tian-Li

doi:10.1145/3071178.3071236

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…As later versions of LTGA, including the one presented in this article, are seen as instances of the GOMEA framework, details will be described in subsequent sections. Besides LTGA which we will from now refer to as LT-GOMEA, other non-EDA algorithms that build models to model and exploit linkage information have been proposed of late [24], [9], [17]. These algorithms that can more generally be described as model-based EAs have also been successful at outperforming hBOA.…”

Section: From Genetic Algorithms To Estimation-of-distribution Algori...mentioning

confidence: 99%

“…GOMEA does not have a traditional selection phase because GOM already induces selection, by discarding changes that are detrimental to a solution's fitness. However, we consider the option of using tournament selection to select good solutions upon which to learn the linkage model, as done in [24], [9]. We remark that with this option, the selection is disregarded after the linkage model is learned, i.e., it is not used to override the population.…”

Section: Gomea With a Traditional Single Populationmentioning

confidence: 99%

“…Finally, we demonstrate the practical applicability of GOMEA by designing parameterless modifications of it. The performance of new and old versions of GOMEA are compared is shown in comparison with other EAs, including, the recent version of DSMGA-II [9], and the Parameterless Population Pyramid (P3) [17]. Moreover, this article joins all algorithmic information from our previous work that is needed to make this article self-contained and represent the current state-of-the-art in the GOMEA research line.…”

Section: Introductionmentioning

confidence: 99%

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Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

Dushatskiy¹,

Virgolin²,

Bouter³

et al. 2021

Preprint

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When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencies between variables, can be key. In this article, we present the latest version of, and propose substantial enhancements to, the Gene-pool Optimal Mixing Evoutionary Algorithm (GOMEA): an EA explicitly designed to estimate and exploit linkage information. We begin by performing a large-scale search over several GOMEA design choices, to understand what matters most and obtain a generally bestperforming version of the algorithm. Next, we introduce a novel version of GOMEA, called CGOMEA, where linkage-based variation is further improved by filtering solution mating based on conditional dependencies. We compare our latest version of GOMEA, the newly introduced CGOMEA, and another contending linkage-aware EA DSMGA-II in an extensive experimental evaluation, involving a benchmark set of 9 black-box problems that can only be solved efficiently if their inherent dependency structure is unveiled and exploited. Finally, in an attempt to make EAs more usable and resilient to parameter choices, we investigate the performance of different automatic population management schemes for GOMEA and CGOMEA, de facto making the EAs parameterless. Our results show that GOMEA and CGOMEA significantly outperform the original GOMEA and DSMGA-II on most problems, setting a new state of the art for the field.

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Section: From Genetic Algorithms To Estimation-of-distribution Algori...mentioning

confidence: 99%

Section: Gomea With a Traditional Single Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

Dushatskiy¹,

Virgolin²,

Bouter³

et al. 2021

Preprint

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show abstract

“…This observation justifies the choice of a DSM-using method as the base of our proposition. proposed [28,45,22,27]. The only technique employed by methods considered in this paper is the linkage tree construction algorithm and hence it is described in details below.…”

Section: Populationmentioning

confidence: 99%

“…Then, the index that has the strongest relation to the last gene in the sequence and is not included in the sequence is added to the sequence. Incremental linkage sets are employed by Dependency Structure Matrix Genetic Algorithm II (DSMGA-II) [28] and Two-edge Dependency Structure Matrix Genetic Algorithm II (DMSGA-IIe) [45] that are presented in the next section.…”

Section: Linkage Treesmentioning

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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Deep Optimisation: Transitioning the Scale of Evolutionary Search by Inducing and Searching in Deep Representations

et al. 2022

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We investigate the optimisation capabilities of an algorithm inspired by the Evolutionary Transitions in Individuality. In these transitions, the natural evolutionary process is repeatedly rescaled through successive levels of biological organisation. Each transition creates new higher-level evolutionary units that combine multiple units from the level below. We call the algorithm Deep Optimisation (DO) to recognise both its use of deep learning methods and the multi-level rescaling of biological evolutionary processes. The evolutionary model used in DO is a simple hill-climber, but, as higher-level representations are learned, the hill-climbing process is repeatedly rescaled to operate in successively higher-level representations. The transition process is based on a deep learning neural network (NN), specifically a deep auto-encoder. Our experiments with DO start with a study using the NP-hard problem, multiple knapsack (MKP). Comparing with state-of-the-art model-building optimisation algorithms (MBOAs), we show that DO finds better solutions to MKP instances and does so without using a problem-specific repair operator. A second, much more in-depth investigation uses a class of configurable problems to understand more precisely the distinct problem characteristics that DO can solve that other MBOAs cannot. Specifically, we observe a polynomial vs exponential scaling distinction where DO is the only algorithm to show polynomial scaling for all problems. We also demonstrate that some problem characteristics need a deep network in DO. In sum, our findings suggest that the use of deep learning principles have significant untapped potential in combinatorial optimisation. Moreover, we argue that natural evolution could be implementing something like DO, and the evolutionary transitions in individuality are the observable result.

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Two-edge graphical linkage model for DSMGA-II

Cited by 21 publications

References 13 publications

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms

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

Deep Optimisation: Transitioning the Scale of Evolutionary Search by Inducing and Searching in Deep Representations

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