Large-scale optimization is vital in today's artificial intelligence (AI) applications for extracting essential knowledge from huge volumes of data. In recent years, to improve the evolutionary algorithms used to ...
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With the increase in the number of objectives, the number of non-dominated solutions will also increase sharply. The sorting method based on the traditional Pareto dominance is not sufficiently distinguishable from th...
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In many applications, the presence of interactions or even mild non-linearities can affect inference and predictions. For that reason, we suggest the use of a class of models laying between statistics and machine lear...
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In many applications, the presence of interactions or even mild non-linearities can affect inference and predictions. For that reason, we suggest the use of a class of models laying between statistics and machine learning and we propose a learning procedure. The models combine a linear part and a tree component that is selected via an evolutionary algorithm, and they can be adopted for any kinds of response, such as, for instance, continuous, categorical, ordinal responses, and survival times. They are inherently interpretable but more flexible than standard regression models, as they easily capture non-linear and interaction effects. The proposed genetic-like learning algorithm allows avoiding a greedy search of the tree component. In a simulation study, we show that the proposed approach has a performance comparable with other machine learning algorithms, with a substantial gain in interpretability and transparency, and we illustrate the method on a real data set.
In recent years, neural architecture search (NAS) has achieved unprecedented development because of its ability to automatically achieve high-performance neural networks in various tasks. Among these, the evolutionary...
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In recent years, neural architecture search (NAS) has achieved unprecedented development because of its ability to automatically achieve high-performance neural networks in various tasks. Among these, the evolutionary neural architecture search (ENAS) has impressed the researchers due to the excellent heuristic exploration capability. However, the evolutionary algorithm-based NAS are prone to the loss of population diversity in the search process, causing that the structure of the surviving individuals is exceedingly similar, which will lead to premature convergence and fail to explore the search space comprehensively and effectively. To address this issue, we propose a novel indicator, named architecture entropy, which is used to measure the architecture diversity of population. Based on this indicator, an effective sampling strategy is proposed to select the candidate individuals with the potential to maintain the population diversity for environmental selection. In addition, an unified encoding scheme of topological structure and computing operation is designed to efficiently express the search space, and the corresponding population update strategies are suggested to promote the convergence. The experimental results on several image classification benchmark datasets CIFAR-10 and CIFAR-100 demonstrate the superiority of our proposed method over the state-of-the-art comparison ones. To further validate the effectiveness of our method in real applications, our proposed NAS method is applied in the identification of lumbar spine X-ray images for osteoporosis diagnosis, and can achieve a better performance than the commonly used methods. Our source codes are available at https://***/ LabyrinthineLeo/AEMONAS.
Cloud computing has revolutionized the provisioning and access of computing resources, offering scalable and flexible alternatives to traditional infrastructure. However, defining how to use these computational resour...
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ISBN:
(纸本)9783031790317;9783031790324
Cloud computing has revolutionized the provisioning and access of computing resources, offering scalable and flexible alternatives to traditional infrastructure. However, defining how to use these computational resources may be challenging. This paper addresses the challenge of workflow scheduling in cloud environments, focusing on Amazon Web Services (AWS) Elastic Compute Cloud (EC2). We present HEACT, a novel approach that integrates a multi-objective evolutionary algorithm with a specialist scheduling heuristic. The evolutionary algorithm is responsible for generating an initial set of machines (with their performance capability and cost information). The set is sent to the specialist scheduling heuristic for efficient task assignment in these machines. Our approach considers fourteen AWS regions, accurate pricing information from AWS, and employs SimGrid to simulate task execution. The proposed method was benchmarked considering established heuristics (HEFT, PEFT, HSIP, MPEFT) and meta-heuristics (NSGA-II, AGEMOEA2). Results demonstrated that the combinations of AGEMOEA2 with MPEFT and AGEMOEA2 with HEFT yield the best performance, indicating AGEMOEA2's efficacy as a state-of-the-art meta-heuristic for workflow scheduling.
Although quantum control typically relies on greedy (local) optimization, traps (irregular critical points) in the control landscape can make optimization hard by foiling local search strategies. We demonstrate the fa...
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Although quantum control typically relies on greedy (local) optimization, traps (irregular critical points) in the control landscape can make optimization hard by foiling local search strategies. We demonstrate the failure of greedy algorithms as well as the (nongreedy) genetic-algorithm method to realize two fast quantum computing gates: a qutrit phase gate and a controlled-not gate. We show that our evolutionary algorithm circumvents the trap to deliver effective quantum control in both instances. Even when greedy algorithms succeed, our evolutionary algorithm can deliver a superior control procedure, for example, reducing the need for high time resolution.
The most popular and successful way to maintain a healthy body is to have a rich and balanced diet combined with physical exercise. Since the diet dilemma was proposed, several works in the literature suggested calcul...
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The most popular and successful way to maintain a healthy body is to have a rich and balanced diet combined with physical exercise. Since the diet dilemma was proposed, several works in the literature suggested calculating a diet that respects the nutritional needs of each person. In the Caloric-Restricted Diet Problem (CRDP), the goal is to find a reduced-calorie diet that meets these nutritional needs, enabling weight loss. This paper proposes an Island-Based Hybrid evolutionary Algorithm (IBHEA) that uses a Genetic Algorithm (GA) and a Differential Evolution (DE) Algorithm with different parameters settings in different islands communicating through several migration policies to solve the CRDP. Computational experiments showed that IBHEA outperformed more than 5% compared with non-distributed and non-hybrid implementations, generating a greater variety of diets with a small calorie count.
作者：
Tian, YePan, JingwenYang, ShangshangZhang, XingyiHe, ShupingJin, YaochuAnhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center
Institute of Artificial Intelligence Hefei230088 China Anhui University
School of Computer Science and Technology Hefei230601 China Anhui University
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University
Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University
Faculty of Technology Bielefeld33619 Germany
The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision s...
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The need for implementing low cost, fully integrated RF wireless transceivers has motivated the widespread use CMOS technology. However, in the particular case for voltage-controlled oscillators (VCO) where ever more ...
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The need for implementing low cost, fully integrated RF wireless transceivers has motivated the widespread use CMOS technology. However, in the particular case for voltage-controlled oscillators (VCO) where ever more stringent specifications in terms of phase-noise must be attained, the design of the on-chip LC tank is a challenging task, where fully advantage of the actual technologies characteristics must be pushed to nearly its limits. To overcome phase-noise limitations arising from the low quality factor of integrated inductors, optimization design methodologies are usually used. In this paper a model-based optimization approach is proposed. In this work the characterization of the oscillator behaviour is guaranteed by a set of analytical models describing each circuit element performance. A set of working examples for UMC130 technology, aiming the minimization of both VCO phase noise and power consumption, is addressed. The results presented, illustrate the potential of a GA optimization procedure design methodology yielding accurate and timely efficient oscillator designs. The validity of the results is checked against HSPICE/RF simulations.
In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most ...
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In this paper the game theory procedures are applied for healthcare monitoring systems and it is analysed using two types of evolutionary algorithms that incorporate Artificial Intelligence (AI) based events. As most of the existing approaches face challenges in establishing real-time connectivity, optimizing decision-making processes, and minimizing latency in Internet of Things (IoT)-based healthcare applications the limitations needs to be addressed. Hence with analytical equivalences that are crucial in game theory, a unique system model is developed using a deterministic framework where four key performers are strategically connected to improve decision-making and security against potential data breaches. By incorporating two evolutionary algorithms, the proposed approach optimizes the state of action for each participant while reducing energy consumption and processing delay. The model is validated through four case studies, demonstrating an average improvement of 60% over existing methodologies. These findings highlight the effectiveness of integrating game theory with evolutionary optimization to enhance real-time healthcare monitoring.
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