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Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年 59卷

作者： Funkner, Anastasia A. Yakovlev, Aleksey N. Kovalchuk, Sergey V. ITMO Univ St Petersburg Russia Almazov Natl Med Res Ctr St Petersburg Russia

The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for predic-tion of the target algorithm's quality and performance. The proposed approach was implemented and investi-gated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was devel-oped to provide a more interpretable prediction of the target algorithm's performance and quality that can be further used for parameter tuning.

关键词： Clinical pathway evolutionary algorithms Knowledge discovery Multi-objective optimization Parameter tuning Predictive modeling Surrogate modeling

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Multispace evolutionary Search for Large-Scale Optimization with Applications to Recommender Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 107-120页

作者： Feng, Liang Shang, Qingxia Hou, Yaqing Tan, Kay Chen Ong, Yew-Soon College of Computer Science Chongqing University Chongqing400044 China College of Computer Science and Technology Dalian University of Technology Dalian116024 China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Centre for Frontier AI Research A STAR Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

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 solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This article, thus, proposes a new search paradigm, namely the multispace evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to five state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems as well as an AI application in e-commerce, i.e., movie recommendation. © 2022 IEEE.

关键词： evolutionary algorithms

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A Practical Approach for Energy Efficient Scheduling in Multicore Environments by Combining evolutionary and YDS algorithms with Faster Energy Estimation 11th

A Practical Approach for Energy Efficient Scheduling in Mult...

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11th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Bankovic, Zorana Liqat, Umer Lopez-Garcia, Pedro IMDEA Software Inst Madrid Spain CSIC Spanish Council Sci Res Madrid Spain

ISBN: (纸本)9783319238685;9783319238678

Energy efficient scheduling and allocation in multicore environments is a well-known NP-hard problem. Nevertheless approximated solutions can be efficiently found by heuristic algorithms, such as evolutionary algorithms (EAs). However, these algorithms have some drawbacks that hinder their applicability: typically they are very slow, and if the space of the feasible solutions is too restricted, they often fail to provide a viable solution. In this paper we propose an approach that overcomes these issues. The approach is based on a custom EA that is fed with predicted information provided by an existing static analysis about the energy consumed by tasks. This solves the time inefficiency problem. In addition, when this algorithm fails to produce a feasible solution, we resort to a modification of the well-known YDS algorithm that we have performed, well adapted to the multicore environment and to the situations when the static power becomes the predominant part. This way, we propose a combined approach that produces an energy efficient scheduling in reasonable time, and always finds a viable solution. The approach has been tested on multicore XMOS chips, but it can easily be adapted to other multicore environments as well. In the tested scenarios the modified YDS can improve the original one up to 20%, while our EA can save 55 - 90% more energy on average than the modified YDS.

关键词： Scheduling energy efficiency multicore systems evolutionary algorithms YDS

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui 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 since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： evolutionary algorithms

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An evolutionary estimation procedure for generalized semilinear regression trees

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COMPUTATIONAL STATISTICS 2023年第4期38卷 1927-1946页

作者： Vannucci, Giulia Gottard, Anna Univ Florence Dept Stat Applicat Comp Sci Florence Italy

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.

关键词： evolutionary algorithms Interpretable statistical learning Semi-parametric regression Regression trunk models

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Architecture entropy sampling-based evolutionary neural architecture search and its application in osteoporosis diagnosis

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COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 213-231页

作者： Chu, Jianjun Yu, Xiaoshan Yang, Shangshang Qiu, Jianfeng Wang, Qijun Second Peoples Hosp Hefei Hefei 230012 Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230039 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China

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.

关键词： Neural architecture search Convolutional neural networks evolutionary algorithms Architecture entropy Medical image recognition

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Optimal LC-VCO design through evolutionary algorithms

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2014年第1期78卷 99-109页

作者： Pereira, P. Helena Fino, M. Ventim-Neves, M. Univ Nova Lisboa CTS Uninova Dept Engn ElectrotecnFCT P-2829516 Caparica Portugal

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.

关键词： LC-VCO Design methodology Discrete-variable optimization evolutionary algorithms

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IoT driven healthcare monitoring with evolutionary optimization and game theory

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Al-Shehari, Taher Alsadhan, Nasser A. Singh, Subav Kebri Dehar Univ Dept Comp Sci Kebri Dehar 250 Ethiopia Panimalar Engn Coll Dept Elect & Commun Engn Chennai India King Saud Univ Dept Selfdev Skill Comp Skills Common Year Deanship 1 Riyadh 11362 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Comp Sci Dept Riyadh 12372 Saudi Arabia Chitkara Univ Chitkara Ctr Res & Dev Baddi 174103 Himachal Prades India Chennai Inst Technol Dept Comp Sci & Engn Chennai India Chitkara Univ Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Uttaranchal Univ Div Res & innovat Dehra Dun India Lovely Profess Univ Div Res & Dev Phagwara Punjab India

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.

关键词： Game theory evolutionary algorithms Internet of things (IoT) Health care

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An island-based hybrid evolutionary algorithm for caloric-restricted diets

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evolutionary INTELLIGENCE 2023年第2期16卷 553-564页

作者： Xavier, Carolina Ribeiro Silva, Joao Gabriel R. Duarte, Grasiele Regina Carvalho, Iago Augusto Vieira, Vinicius da Fonseca Goliatt, Leonardo Univ Fed Sao Joao del Rei Comp Sci Program Sao Joao Del Rei Brazil Univ Fed Juiz de Fora Computat Modeling Program Juiz De Fora Brazil COPPE Fed Univ Rio de Janeiro Civil Engn Program Rio De Janeiro Brazil Univ Fed Alfenas Comp Sci Dept Av Jovino Fernandes Sales 2600 BR-37133840 Alfenas Brazil

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.

关键词： Obesity Diet problem evolutionary algorithms Island model Genetic algorithm Differential evolution Caloric-restricted diet problem

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Distance-Targeting Mutation Operator for evolutionary Design of 3D Structures

Distance-Targeting Mutation Operator for Evolutionary Design...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Komosinski, Maciej Mensfelt, Agnieszka Poznan Univ Tech Inst Comp Sci Poznan Poland Royal Holloway Univ London Dept Comp Sci Egham Surrey England

ISBN: (纸本)9798400704949

evolutionary design of 3D structures - an automated design by the methods of evolutionary algorithms - is a hard optimization problem. One of the contributing factors is a complex genotype-to-phenotype mapping often associated with the genetic representations of the designs. In such case, the genetic operators may exhibit low locality, i.e., a small change introduced in a genotype may result in a significant change in the phenotype and its fitness, hampering the search process. To overcome this challenge in evolutionary design, we introduce the Distance-Targeting Mutation Operator (DTM). The aim of this operator is to create offspring whose distance to the parent solution, according to a selected dissimilarity measure, approximates a predefined value. We compare the performance of the DTM operator to the performance of the mutation operator without parent-offspring distance control in a series of evolutionary experiments. We use different genetic representations, dissimilarity measures, and optimization goals, including velocity and height of active and passive 3D structures. The introduced DTM operator outperforms the standard one in terms of best fitness in most of the considered cases.

关键词： evolutionary algorithms mutation operator evolutionary design

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