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Convolutional Neural Networks-Based Lung Nodule Classification: A Surrogate-Assisted evolutionary algorithm for Hyperparameter Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2021年第5期25卷 869-882页

作者： Zhang, Miao Li, Huiqi Pan, Shirui Lyu, Juan Ling, Steve Su, Steven Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Monash Univ Fac Informat Technol Melbourne Vic 3800 Australia Harbin Engn Univ Informat & Commun Engn Harbin 150001 Peoples R China

This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-assisted evolutionary algorithm has been recently introduced to automatically search for optimal hyperparameter configurations of DNNs, by applying computationally efficient surrogate models to approximate the validation error function of hyperparameter configurations. Different from existing surrogate models adopting stationary covariance functions (kernels) to measure the difference between hyperparameter points, this article proposes a nonstationary kernel that allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs. A multilevel convolutional neural network (ML-CNN) is built for lung nodule classification, and the hyperparameter configuration is optimized by the proposed nonstationary kernel-based Gaussian surrogate model. Our algorithm searches with a surrogate for optimal setting via a hyperparameter importance-based evolutionary strategy, and the experiments demonstrate our algorithm outperforms manual tuning and several well-established hyperparameter optimization methods, including random search, grid search, the tree-structured parzen estimator (TPE) approach, Gaussian processes (GP) with stationary kernels, and the recently proposed hyperparameter optimization via RBF and dynamic (HORD) coordinate search.

关键词： Optimization Kernel Neural networks Lung Feature extraction Gaussian processes Convolutional neural networks AutoML evolutionary algorithm hyperparameter optimization lung nodule classification nonstationary kernel

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An evolutionary algorithm for multi-criteria inverse optimal value problems using a bilevel optimization model

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APPLIED SOFT COMPUTING 2014年 23卷 308-318页

作者： Li, Hecheng Qinghai Normal Univ Key Lab Tibetan Informat Proc Minist Educ Dept Math Xining 810008 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

Given a linear program, a desired optimal objective value, and a set of feasible cost vectors, one needs to determine a cost vector of the linear program such that the corresponding optimal objective value is closest to the desired value. The problem is always known as a standard inverse optimal value problem. When multiple criteria are adopted to determine cost vectors, a multi-criteria inverse optimal value problem arises, which is more general than the standard case. This paper focuses on the algorithmic approach for this class of problems, and develops an evolutionary algorithm based on a dynamic weighted aggregation method. First, the original problem is converted into a bilevel program with multiple upper level objectives, in which the lower level problem is a linear program for each fixed cost vector. In addition, the potential bases of the lower level program are encoded as chromosomes, and the weighted sum of the upper level objectives is taken as a new optimization function, by which some potential nondominated solutions can be generated. In the design of the evolutionary algorithm some specified characteristics of the problem are well utilized, such as the optimality conditions. Some preliminary computational experiments are reported, which demonstrates that the proposed algorithm is efficient and robust. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Inverse optimal value problem Bilevel program evolutionary algorithm Solutions

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Composition of Resource-Service Chain Based on evolutionary algorithm in Distributed Cloud Manufacturing Systems

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IEEE ACCESS 2020年 8卷 19911-19920页

作者： Li, Haibo Weng, Shaoyuan Tong, Juncheng He, Ting Chen, Wenyun Sun, Mengmeng Shen, Yingtong Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Xiamen Engn Res Ctr Enterprise Interoperabil & Bu Xiamen 361021 Peoples R China

In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.

关键词： Distributed cloud manufacturing resource-service chain composition evolutionary algorithm business process

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MEAMVC: A Membrane evolutionary algorithm for Solving Minimum Vertex Cover Problem

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IEEE ACCESS 2019年 7卷 60774-60784页

作者： Guo, Ping Quan, Changsheng Chen, Haizhu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Software Theory & Technol Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Coll Elect Engn Dept Software Engn Chongqing 401331 Peoples R China

Since the membrane algorithm was proposed, it has been used for many optimization problems such as, traveling salesman problem, the knapsack problem, and so on. In membrane algorithms, the membranes have two functions: container and comparator. As a container, each membrane contains one evolutionary algorithm like genetic algorithm and ant colony algorithm. These algorithms are called sub-algorithms and used to evolve individuals. As a comparator, the membrane will compare the results of sub-algorithms, and select the best as the base of the next evolvement. This paper proposes a novel evolutionary algorithm called membrane evolutionary algorithm framework (MEAF). Unlike the presented membrane algorithms, the membranes in MEAF will be evolved to solve problems by using four operators that are abstracted from the life cycle of living cells. Based on MEAF, a membrane evolutionary algorithm called MEAMVC is proposed to solve the minimum vertex cover (MVC) problem. The experimental results show the advantages of MEAMVC when MEAMVC is compared with two state-of-the-art MVC algorithms proposed in recent years.

关键词： evolutionary algorithm membrane computing membrane evolutionary algorithm framework minimum vertex cover problem

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MOEA/D: A multiobjective evolutionary algorithm based on decomposition

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2007年第6期11卷 712-731页

作者： Zhang, Qingfu Li, Hui Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously. Each subproblem is optimized by only using information from its several neighboring subproblems, which makes MOEA/D have lower computational complexity at each generation than MOGLS and nondominated sorting genetic algorithm H (NSGA-II). Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjective optimization problems. It has been shown that MOEA/D using objective normalization can deal with disparately-scaled objectives, and MOEA/D with an advanced decomposition method can generate a set of very evenly distributed solutions for 3-objective test instances. The ability of MOEA/D with small population, the scalability and sensitivity of MOEA/D have also been experimentally investigated in this paper.

关键词： computational complexity decomposition evolutionary algorithm multiobjective optimization Pareto optimality

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Improving decomposition-based multiobjective evolutionary algorithm with local reference point aided search

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INFORMATION SCIENCES 2021年 576卷 557-576页

作者： Jiang, Jing Han, Fei Wang, Jie Ling, Qinghua Han, Henry Fan, Zizhu Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Anqing Normal Univ Sch Comp & Informat Anqing 246133 Anhui Peoples R China Jiangsu Key Lab Secur Technol Ind Cyberspace Zhenjiang 212013 Jiangsu Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci Zhenjiang 212003 Jiangsu Peoples R China Fordham Univ Dept Comp & Informat Sci New York NY USA East China Jiaotong Univ Sch Basic Sci Nanchang 330013 Jiangxi Peoples R China

ABSTR A C T Due to the fixed and monotonous search direction, the performance of decomposition-based multiobjective evolutionary algorithms (MOEAs) highly depends on the Pareto front (PF) shape. Recent studies have highlighted the complementary effect of the ideal and nadir points. They roughly employed both as the reference points to diversify the search direction. However, few works investigate whether two points are equally important. This paper thereby proposes a novel decomposition-based MOEA, where the ideal point is consistently considered as the global reference point while the nadir point is condition-ally employed as the local one. We show that the nadir point may aid the ideal point in some cases and be recognized as a redundant one in others. More specifically, an assign-ment strategy is suggested to determine the necessity of using a local reference point for each subproblem, by considering whether the solution found by the nadir point and corre-sponding weight vector can improve the quality of the population. Experimental results finally verify the effectiveness of the proposed algorithm on 57 benchmark test problems with various PF shapes. In comparison with the state-of-the-art decomposition-based MOEAs, the proposed algorithm is promising to bring a more refined search and prevent redundant search behaviors. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Multiobjective optimization evolutionary algorithm Decomposition Ideal and nadir points Local reference point

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A hybrid self-adaptive evolutionary algorithm for marker optimization in the clothing industry

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APPLIED SOFT COMPUTING 2010年第2期10卷 409-422页

作者： Fister, Iztok Mernik, Marjan Filipic, Bogdan Jozef Stefan Inst Dept Intelligent Syst SI-1000 Ljubljana Slovenia Univ Maribor Fac Elect Engn & Comp Sci SI-2000 Maribor Slovenia Mura European Fash Design SI-9000 Murska Sobota Slovenia

The task of marker optimization in clothing production is to eliminate pieces from a work order using an optimal sequence of markers and plies, where the work order is given as a matrix of colors by sizes, markers are vectors of sizes to be laid-out and cut together, and the number of plies determines how many pieces are eliminated from the work order each time. Although the optimality of a marker sequence can be determined in several ways, we consider minimum preparation cost as a key objective in clothing production. The traditional algorithms and the simple evolutionary algorithms used in marker optimization today have relied on minimizing the number of markers, which only indirectly reduces production costs. In this paper we propose a hybrid self-adaptive evolutionary algorithm (HSA-EA) for marker optimization that improves the results of the previous algorithms and successfully deals with the objective of minimum preparation cost. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Clothing production Marker optimization 0/1 knapsack problem NP-hard problem evolutionary algorithm Self-adaptation Heuristics Hybridization

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An evolutionary algorithm with a history mechanism for tuning a chess evaluation function

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APPLIED SOFT COMPUTING 2013年第7期13卷 3234-3247页

作者： Vazquez-Fernandez, Eduardo Coello Coello, Carlos A. Sagols Troncoso, Feliu D. CINVESTAV IPN Dept Comp Sci Evolutionary Computat Grp Mexico City 07300 DF Mexico CINVESTAV IPN Dept Math Mexico City 07360 DF Mexico

Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the "good" virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory. (C) 2013 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithm evolutionary programming Chess engine Artificial intelligence Chess evaluation function

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An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2021年第5期20卷 2525-2539页

作者： Chauhan, Sumika Singh, Manmohan Kumar Aggarwal, Ashwani St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India

The bearing vibration signal possesses nonlinear and non-stationary characteristics;hence;it is difficult to diagnosis the faults in the bearing under different working conditions. In this article, a new scheme has been proposed based on complete ensemble empirical mode decomposition with adaptive noise and corrected conditional entropy to recognize the different class of faults in bearing. The mode with minimum corrected conditional entropy is treated as a prominent mode from which sensitive features are extracted. A filter-based feature selection scheme is used for the same and for ranking the features based on variance to reduce the redundancy of data set. This data set is made input to support vector machine. The performance of the support vector machine classifier is improved by optimizing its parameters to obtain maximum classification accuracy. To address this issue, an evolutionary algorithm (diversity-driven multi-parent evolutionary algorithm) is used. With optimized support vector machine parameters, the support vector machine is trained to build a classification model with 10-fold cross-validation. After training, the built model is tested against test data set for fitness evaluation. The support vector machine classifier gives 100% accuracy at regularization and kernel parameter's value of 1.3343 and of 782.6329, respectively, with 27.93 s of training time for a single iteration.

关键词： Support vector machine corrected conditional entropy ensemble empirical mode decomposition with adaptive noise evolutionary algorithm

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Hybrid evolutionary algorithm and application to structural optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2005年第3期30卷 219-226页

作者： Fawaz, Z Xu, YG Behdinan, K Ryerson Univ Dept Aerosp Engn Toronto ON M5B 2K3 Canada

This paper introduces a new evolutionary algorithm with a globally stochastic but locally heuristic search strategy. It is implemented by incorporating a modified micro-genetic algorithm with two local optimization operators. Performance tests using two benchmarking functions demonstrate that the new algorithm has excellent convergence performance when applied to multimodal optimization problems. The number of objective function evaluations required to obtain global optima is only 3.5-3.7% of that of using the conventional micro-genetic algorithm. The new algorithm is used to optimize the design of an 18-bar truss, with the aim of minimizing its weight while meeting the stress, section area, and geometry constraints. The corresponding optimal design is obtained with considerably fewer computational operations than required for the existing algorithms.

关键词： evolutionary algorithm hybrid genetic algorithm structural optimization

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