An Evolutionary Constraint-Handling Technique for Parametric Optimization of a Cancer Immunotherapy Model

Xu, Weinan; Xu, Jian‐Xin; He, Defeng; Tan, Kay Chen

doi:10.1109/tetci.2018.2880516

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…For other MEC algorithms, apart from the MGA, MPSO, MDE, and MEDA detailed in this paper, the implementation of matrix-based ACO and others are worthy studied. Moreover, when based on special EC algorithms for special optimization problems, the special matrix-based evolutionary operators in dealing with large-scale [48], dynamic [49], multimodal [50], multi-objective [51], many-objective [52], and constrained issues [53] are also worthy studied.…”

Section: Discussionmentioning

confidence: 99%

Matrix-Based Evolutionary Computation

Zhan

Zhang

Lin

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Computational intelligence (CI), including artificial neural network, fuzzy logic, and evolutionary computation (EC), has rapidly developed nowadays. Especially, EC is a kind of algorithm for knowledge creation and problem solving, playing a significant role in CI and artificial intelligence (AI). However, traditional EC algorithms have faced great challenge of heavy computational burden and long running time in large-scale (e.g., with many variables) problems. How to efficiently extend EC algorithms to solve complex problems has become one of the most significant research topics in CI and AI communities. To this aim, this paper proposes a matrix-based EC (MEC) framework to extend traditional EC algorithms for efficiently solving large-scale or super large-scale optimization problems. The proposed framework is an entirely new perspective on EC algorithm, from the solution representation to the evolutionary operators. In this framework, the whole population (containing a set of individuals) is defined as a matrix, where a row stands for an individual and a column stands for a dimension (decision variable). This way, the parallel computing functionalities of matrix can be directly and easily carried out on the high performance computing resources to accelerate the computational speed of evolutionary operators. This paper gives two typical examples of MEC algorithms, named matrix-based genetic algorithm and matrix-based particle swarm optimization. Their matrix-based solution representations are presented and the evolutionary operators based on the matrix are described. Moreover, the time complexity is analyzed and the experiments are conducted to show that these MEC algorithms are efficient in reducing the computational time on large scale of decision variables. The MEC is a promising way to extend EC to complex optimization problems

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Section: Discussionmentioning

confidence: 99%

Matrix-Based Evolutionary Computation

Zhan

Zhang

Lin

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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“…Higher values of these indicators imply better performance. These metrics are denoted with the given Equation (19), where is the amount of negative instances that are misclassified; is the amount of positive instances that are classified properly;…”

Section: Comparisons Of Different Generation Methodsmentioning

confidence: 99%

“…A time series-histogram method is presented to predict the remaining useful life of aero-engine gears by extracting features of event data [18]. In these methods, sample generation is the key; however, on the one hand, oversampling methods that are widely used to produce sufficient samples by learning the location relationship of the original data [19], like random oversampling [20] and synthetic minority over-sampling technique [21], generate samples inside the ranges of the original data without consideration of the correlations among dimensions of the original data and instead deemed as independent. On the other hand, because the initial values of gear parameters in a test rig are empirically based on an engineer's experience, collected real-world gear data are highly dense.…”

Section: Introductionmentioning

confidence: 99%

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

Zhao

et al. 2021

Actuators

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Gear reliability assessment of vehicle transmission has been a challenging issue of determining vehicle safety in the transmission industry due to a significant amount of classification errors with high-coupling gear parameters and insufficient high-density data. In terms of the preprocessing of gear reliability assessment, this paper presents a representation generation approach based on generative adversarial networks (GAN) to advance the performance of reliability evaluation as a classification problem. First, with no need for complex modeling and massive calculations, a conditional generative adversarial net (CGAN) based model is established to generate gear representations through discovering inherent mapping between features with gear parameters and gear reliability. Instead of producing intact samples like other GAN techniques, the CGAN based model is designed to learn features of gear data. In this model, to raise the diversity of produced features, a mini-batch strategy of randomly sampling from the combination of raw and generated representations is used in the discriminator, instead of using all of the data features. Second, in order to overcome the unlabeled ability of CGAN, a Wasserstein labeling (WL) scheme is proposed to tag the created representations from our model for classification. Lastly, original and produced representations are fused to train classifiers. Experiments on real-world gear data from the industry indicate that the proposed approach outperforms other techniques on operational metrics.

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“…In the automatic control field, Wang et al (2020a) applied the DE-based algorithm to the gait optimization of humanoid robots, which was restricted by the rotation angle of each degree of freedom. Xu et al (2019) proposed a new CHT that divided the population into different sections and applied it to the constrained parametric optimization for a breast cancer immunotherapy model. As for resource scheduling, Chen and Chou (2017) proposed the NSGA-II to solve airline crew roster recovery problems, which had a set of constraints for safety.…”

Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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An Evolutionary Constraint-Handling Technique for Parametric Optimization of a Cancer Immunotherapy Model

Cited by 7 publications

References 41 publications

Matrix-Based Evolutionary Computation

Matrix-Based Evolutionary Computation

A Representation Generation Approach of Transmission Gear Based on Conditional Generative Adversarial Network

A survey on evolutionary computation for complex continuous optimization

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