•A new hybrid algorithm AC-PGA is designed for solving large scale MTSPs.•AC-PGA has better performance than some existing algorithms.•AC-PGA has weak dependence on the initial value.•A path matrix ...representation method is introduced for dealing with MTSPs.
Multiple traveling salesmen problem (MTSP) is not only a generalization of the traveling salesman problem (TSP), but also more suitable for modeling practical problems in the real life than TSP. For solving the MTSP with multiple depots, the requirement of minimum and maximum number of cities that each salesman should visit, a hybrid algorithm called ant colony-partheno genetic algorithms (AC-PGA) is provided by combining partheno genetic algorithms (PGA) and ant colony algorithms (ACO). The main idea in this paper is to divide the variables into two parts. In detail, it exploits PGA to comprehensively search the best value of the first part variables and then utilizes ACO to accurately determine the second part variables value. For comparative analysis, PGA, improved PGA (IPGA), two-part wolf pack search (TWPS), artificial bee colony (ABC) and invasive weed optimization (IWO) algorithms are adopted to solve MTSP and validated with publicly available TSPLIB benchmarks. The results of comparative experiments show that AC-PGA is sufficiently effective in solving large scale MTSP and has better performance than the existing algorithms.
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•State-of-the-art results on the C-MAPSS dataset.•Genetic algorithm effectively tunes hyper-parameters in deep architectures.•Unsupervised pre-training extracts degradation related ...features.•Semi-supervised learning improves the remaining useful life prediction accuracy.
In recent years, research has proposed several deep learning (DL) approaches to providing reliable remaining useful life (RUL) predictions in Prognostics and Health Management (PHM) applications. Although supervised DL techniques, such as Convolutional Neural Network and Long-Short Term Memory, have outperformed traditional prognosis algorithms, they are still dependent on large labeled training datasets. With respect to real-life PHM applications, high-quality labeled training data might be both challenging and time-consuming to acquire. Alternatively, unsupervised DL techniques introduce an initial pre-training stage to extract degradation related features from raw unlabeled training data automatically. Thus, the combination of unsupervised and supervised (semi-supervised) learning has the potential to provide high RUL prediction accuracy even with reduced amounts of labeled training data. This paper investigates the effect of unsupervised pre-training in RUL predictions utilizing a semi-supervised setup. Additionally, a Genetic Algorithm (GA) approach is applied in order to tune the diverse amount of hyper-parameters in the training procedure. The advantages of the proposed semi-supervised setup have been verified on the popular C-MAPSS dataset. The experimental study, compares this approach to purely supervised training, both when the training data is completely labeled and when the labeled training data is reduced, and to the most robust results in the literature. The results suggest that unsupervised pre-training is a promising feature in RUL predictions subjected to multiple operating conditions and fault modes.
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Completely Automated CNN Architecture Design Based on Blocks Sun, Yanan; Xue, Bing; Zhang, Mengjie ...
IEEE transaction on neural networks and learning systems,
2020-April, 2020-Apr, 2020-4-00, 20200401, Volume:
31, Issue:
4
Journal Article
Open access
The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the ...investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.
Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, ...there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first transformed into a deterministic bi-objective problem, where new objectives are hyper-volume and imprecision. A set-based Pareto dominance relation was then defined to modify the fast nondominated sorting approach in NSGA-II. Additionally, set-based evolutionary schemes were suggested. Finally, our method was empirically evaluated on 39 benchmark IMaOPs as well as a car cab design problem and compared with two typical methods. The numerical results demonstrated the superiority of our method and indicated that a tradeoff approximate front between convergence and uncertainty can be produced.
Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network ...(BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the proposed hybrid model (PHM) is determined by genetic algorithm (GA). The closing of the Shenzhen Integrated Index (SZII) and opening of the Dow Jones Industrial Average Index (DJIAI) are used as illustrative examples to evaluate the performances of the PHM. Numerical results show that the proposed model outperforms all traditional models, including ESM, ARIMA, BPNN, the equal weight hybrid model (EWH), and the random walk model (RWM).
► A hybrid model combining ESM, ARIMA, and BPNN is proposed for forecasting stock indices. ► The weight of the proposed hybrid model is determined by genetic algorithm. ► The real dataset for stock indices are used as illustrative examples to show the better performances of the hybrid model.
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Parent-centric real-parameter crossover operators create the offspring in the neighbourhood of one of the parents, the female parent. The other parent, the male one, defines the range of the ...neighbourhood. With the aim of improving the behaviour of these crossover operators, we present three processes that are performed before their application. First, a female and male differentiation process determines the individuals in the population that may become female or/and male parents. Then, two different selection mechanisms choose the female and male parents from each group. In addition, we tackle the election of the most adequate evolution model to take out profit from these parent selection mechanisms. The experimental results confirm that these three processes may enhance the operation of the parent-centric crossover operators.
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Population diversity is essential for avoiding premature convergence in genetic algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in populations are ...not well understood. We use rigorous runtime analysis to gain insight into population dynamics and GA performance for the (<inline-formula> <tex-math notation="LaTeX">{\mu +1} </tex-math></inline-formula>) GA and the Jump test function. We show that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity. This leads to significant improvements of the expected optimization time compared to mutation-only algorithms like the (1 + 1) evolutionary algorithm. Moreover, increasing the mutation rate by an arbitrarily small constant factor can facilitate the generation of diversity, leading to even larger speedups. Experiments were conducted to complement our theoretical findings and further highlight the benefits of crossover on the function class.
•The bank lending decisions in credit crunch environments are big challenge.•This NP-hard optimization problem is solved using a proposed GA based model.•The proposed model is tested using two ...scenarios with simulated and real data.•The real data is collected from Southern Louisiana Credit Union.•The proposed model increased the bank profit and improved the system performance.
To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.
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Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For ...the most state-of-the-art CNNs, their architectures are often manually designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this article, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its "automatic" characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, compared to the state-of-the-art peer competitors covering eight manually designed CNNs, seven automatic + manually tuning, and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers, and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
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