evolutionary multitask optimization (EMTO) has been increasingly employed in addressing high-dimensional feature selection challenges, but current EMTO algorithms still have three deficiencies: First, in task generati...
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ISBN:
(纸本)9798400704949
evolutionary multitask optimization (EMTO) has been increasingly employed in addressing high-dimensional feature selection challenges, but current EMTO algorithms still have three deficiencies: First, in task generation, they just consider the linear correlation among features but ignore the nonlinear correlation. Second, they commonly encounter negative knowledge transfer. Third, they are hard to strike an optimal balance between global search capability and computational efficiency. This paper proposes an adaptive aggregative multitask competitive swarm optimization (AAMCSO) for high-dimensional feature selection, which contains three novel and effective strategies to address the above deficiencies. Firstly, AAMCSO proposes a linear-and-nonlinear-correlation-based task generation strategy to generate multiple tasks while considering both linear and nonlinear correlation between features and labels. Secondly, AAMCSO proposes an adaptive aggregative knowledge transfer strategy to adaptively transfer positive knowledge among related tasks. Thirdly, AAMCSO proposes a bi-directional asymmetric flip strategy to guide the population to search for a smaller feature subset with better classification performance. We have conducted extensive comparative experiments on AAMCSO and multiple state-of-the-art feature selection algorithms in high-dimensional feature selection problems with up to 10000 dimensions. The results show that AAMCSO achieves significantly superior performance to the state-of-the-art comparison algorithms in terms of both classification accuracy and feature number.
For machine learning tasks, the explosive growth of data makes feature selection be an essential preprocessing step, as it improves generalization capability, reduces run-time and model's complexity. Traditional f...
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ISBN:
(纸本)9798350362770;9798350362763
For machine learning tasks, the explosive growth of data makes feature selection be an essential preprocessing step, as it improves generalization capability, reduces run-time and model's complexity. Traditional feature selection methods select the informative subset to facilitate the classification accuracy. However, in real applications, the cost of collecting the features shall be taken into account. This paper proposes a new costsensitive feature selection method based on an adaptive optimization framework. Unlike most of the existing methods simply adding or deleting features one by one, the proposed method uses an adaptive swarm intelligence algorithm to search the optimal subset. This algorithm achieves a more reasonable balance between the exploration and exploitation utilizing a cosine congestion factor, and is employed in cost-sensitive feature selection problem. Both of test cost and misclassification cost are considered, then a new and more reasonable comprehensive evaluation criteria is proposed. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed methods.
The von-Neumann architecture has a bottleneck which limits the speed at which data can be made available for computation. To combat this problem, novel paradigms for computing are being developed. One such paradigm, k...
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ISBN:
(纸本)9798350387186;9798350387179
The von-Neumann architecture has a bottleneck which limits the speed at which data can be made available for computation. To combat this problem, novel paradigms for computing are being developed. One such paradigm, known as in-memory computing, interleaves computation with the storage of data within the same circuits. MAGIC, or Memristor Aided Logic, is an approach which uses memory circuits which physically perform computation through write operations to memory. Sequencing these operations is a computationally difficult problem which is directly correlated with the cost of solutions using MAGIC based in-memory computation. SAGA models the execution sequences as a topological sorting problem which makes the optimization well-suited for genetic algorithms. We then detail the formation and implementation of these genetic algorithms and evaluate them over a number of open circuit implementations. The memory-footprint needed for evaluating each of these circuits is decreased by up to 52% from existing, greedy-algorithm-based optimization solutions. Over the 10 benchmark circuits evaluated, these modifications lead to an overall improvement in the efficiency of in-memory circuit evaluation of 128% in the best case and 27.5% on average.
In the event of failures, it is essential that the distribution network can autonomously adjust its topology structure to satisfy the power supply requirements. Therefore, how to reconstruct the distribution network i...
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ISBN:
(纸本)9789819771806;9789819771813
In the event of failures, it is essential that the distribution network can autonomously adjust its topology structure to satisfy the power supply requirements. Therefore, how to reconstruct the distribution network is crucial for the development of smart grids. To improve the accuracy and reliability of fault reconstruction in distribution networks, we propose a discrete multi-modal multi-objective particle swarm algorithm based on the nearest neighbor algorithm (DMMPSO-NNS) in this study, which employs the nearest neighbor search method to maintain population diversity. The results prove that the DMMPSO-NNS performs better than other selected algorithms on the IEEE 33-bus distribution network.
The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligence applications characterized by high-impact applications generating text, images, and videos. However, these models us...
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ISBN:
(纸本)9798350388497;9798350388480
The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligence applications characterized by high-impact applications generating text, images, and videos. However, these models usually ensue with one critical challenge called hallucination: confident presentation of inaccurate or fabricated information. This problem attracts serious concern when these models are applied to specialized domains, including healthcare and law, where the accuracy and preciseness of information are absolute conditions. In this paper, we propose EvoLLMs, an innovative framework inspired by evolutionary computation, which automates the generation of high-quality Question-answering (QA) datasets while minimizing hallucinations. EvoLLMs employs genetic algorithms, mimicking evolutionary processes like selection, variation, and mutation, to guide LLMs in generating accurate, contextually relevant question-answer pairs. Comparative analysis shows that EvoLLMs consistently outperforms human-generated datasets in key metrics such as Depth, Relevance, and Coverage, while nearly matching human performance in mitigating hallucinations. These results highlight EvoLLMs as a robust and efficient solution for QA dataset generation, significantly reducing the time and resources required for manual curation.
Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments(su...
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Satellites based positioning has been widely applied to many areas in our daily lives and thus become indispensable, which also leads to increasing demand for high-positioning accuracy. In some complex environments(such as dense urban, valley), multipath interference is one of the main error sources deteriorating positioning accuracy, and it is difficult to eliminate via differential techniques due to its uncertainty of occurrence and irrelevance in different instants. To address this problem,we propose a positioning method for global navigation satellite systems(GNSS) by adopting a modified teachinglearning based optimization(TLBO) algorithm after the positioning problem is formulated as an optimization problem. Experiments are conducted by using actual satellite data. The results show that the proposed positioning algorithm outperforms other algorithms, such as particle swarm optimization based positioning algorithm,differential evolution based positioning algorithm, variable projection method, and TLBO algorithm, in terms of accuracy and stability.
Survival analysis focuses on the prediction of failure time and serves as an important prognostic tool, not solely confined to medicine but also across diverse fields. Machine learning methods, especially decision tre...
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ISBN:
(纸本)9783031637711;9783031637728
Survival analysis focuses on the prediction of failure time and serves as an important prognostic tool, not solely confined to medicine but also across diverse fields. Machine learning methods, especially decision trees, are increasingly replacing traditional statistical methods which are based on assumptions that are often difficult to meet. The paper presents a new global method for inducing survival trees containing Kaplan-Mayer estimators in leaves. Using a specialized evolutionary algorithm, the method searches for oblique trees in which multivariate tests in internal nodes divide the feature space using hyperplanes. Specific variants of mutation and crossover operators have been developed, making evolution effective and efficient. The fitness function is based on the integrated Brier score and prevents overfitting taking into account the size of the tree. A preliminary experimental verification and comparison with classical univariate trees was carried out on real medical datasets. The evaluation results are promising.
Solving dynamic optimization problems is always a challenging task. For this purpose, heuristic and metaheuristic methods are the preferred ones, especially in the context of real-time decision making. In this sense, ...
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ISBN:
(纸本)9798350365948;9798350365931
Solving dynamic optimization problems is always a challenging task. For this purpose, heuristic and metaheuristic methods are the preferred ones, especially in the context of real-time decision making. In this sense, it is necessary to have evaluation mechanisms for such methods, in order to assist in the selection of one of them to be applied in a particular reality. In this paper we propose metrics for the evaluation of methods that solve real-time dynamic problems that do not require complete knowledge of the search space. These metrics can be used to compare methods quantitatively in various ways. Besides, the application of the proposal on variants of Ant Systems in a classical combinatorial optimization problem such as the Vehicule Routing Problem (VRP) is studied.
Automated planning and scheduling aims to design intelligent algorithms that could allocate resources for solving a given planning and scheduling problem and meet specific purposes. The current status of swarm intelli...
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ISBN:
(纸本)9789819771837;9789819771844
Automated planning and scheduling aims to design intelligent algorithms that could allocate resources for solving a given planning and scheduling problem and meet specific purposes. The current status of swarm intelligence on planning and scheduling tasks is discussed and the challenges of swarm intelligence and automated planning algorithms design are analyzed in this paper. Interpretability is the foundation of understanding problems' properties and algorithms' search behaviors. The interpretability of algorithms is discussed from interpretable processes and interpretable results. Based on the interpretability analysis, swarm intelligence's evolution and learning ability could design the automated algorithm.
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