Direct Memory Schemes for Population-Based Incremental Learning in Cyclically Changing Environments

Mavrovouniotis, Michalis; Yang, Shengxiang

doi:10.1007/978-3-319-31153-1_16

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…In [27], the author proposed a PBIL variant with a memory scheme to solve cyclically changing DOPs. In MGA, the GA part is used to carry out the optimization process, BOA is used for (i): determining phenotype values, and (ii): creating immigrants by sampling new individuals using its own BN.…”

Section: Most Recent Work On Dynamic Optimizationmentioning

confidence: 99%

Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

Gazioğlu

Etaner-Uyar

2022

Engineering Science and Technology, an International Journal

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Section: Most Recent Work On Dynamic Optimizationmentioning

confidence: 99%

Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

Gazioğlu

Etaner-Uyar

2022

Engineering Science and Technology, an International Journal

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“…In contrast, memory can also be maintained explicitly. For example, [50][51][52] directly stored the previous promising solutions or local optima visited in the previous time steps to inform the current search process. [53] exploited the historical knowledge and used it to build up an evolutionary environment model.…”

Section: Evolutionary Optimizationmentioning

confidence: 99%

A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Li¹,

Chen²,

Yao³

2022

Preprint

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Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. This paper proposes a simple but effective transfer learning framework to empower data-driven evolutionary optimization to solve dynamic optimization problems. Specifically, it applies a hierarchical multi-output Gaussian process to capture the correlation between data collected from different time steps with a linearly increased number of hyperparameters. Furthermore, an adaptive source task selection along with a bespoke warm staring initialization mechanisms are proposed to better leverage the knowledge extracted from previous optimization exercises. By doing so, the data-driven evolutionary optimization can jump start the optimization in the new environment with a strictly limited computational budget. Experiments on synthetic benchmark test problems and a real-world case study demonstrate the effectiveness of our proposed algorithm against nine state-of-the-art peer algorithms.

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“…The reuse of learnt knowledge for an enhanced search performance in dynamic optimization problems has been investigated by using different memory schemes [23], [24]. Feng et al [25] proposed a new memetic computation paradigm [26] to transfer the learned knowledge from previously solved problems in order to improve future evolutionary searches.…”

Section: A Transfer Learning In Evolutionary Computationmentioning

confidence: 99%

Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

Iqbal

Xue

Al-Sahaf

et al. 2017

IEEE Trans. Evol. Computat.

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Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimisation, image analysis and classification. Transfer learning is a type of machine learning approach that can be used to solve complex tasks. Transfer learning has been introduced to genetic programming to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with genetic programming has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and genetic programming to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.

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Direct Memory Schemes for Population-Based Incremental Learning in Cyclically Changing Environments

Cited by 6 publications

References 8 publications

Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

Experimental analysis of a statistical multiploid genetic algorithm for dynamic environments

A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments

Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

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