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Academic literature on the topic 'Intelligence algorithm'

Author: Grafiati

Published: 4 June 2025

Last updated: 6 July 2025

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Journal articles on the topic "Intelligence algorithm"

1

Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.

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In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.

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Navarrete-Dechent, Cristian. "Teledermatology and Artificial Intelligence." Iproceedings 8, no. 1 (2022): e36894. http://dx.doi.org/10.2196/36894.

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Background The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest None declared.

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Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.

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This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.

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Al-Obaidi, Ahmed T. Sadiq, Hasanen S. Abdullah, and Zied O. Ahmed. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp354-360.

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<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

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Ahmed, T. Sadiq Al-Obaidi, S. Abdullah Hasanen, and O. Ahmed Zied. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354–60. https://doi.org/10.11591/ijeecs.v10.i1.pp354-360.

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Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.

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Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.

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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.

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Pu, Kang. "Intellectual property protection for AI algorithms." Frontiers in Computing and Intelligent Systems 2, no. 3 (2023): 44–47. http://dx.doi.org/10.54097/fcis.v2i3.5210.

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In the 21st century, artificial intelligence technology is developing rapidly. Artificial intelligence technology combines multiple disciplines, involves multiple industries, and absorbs multiple talents, which is an extremely complex and huge technology. As the core technology of artificial intelligence, artificial intelligence algorithms have the ability to train and learn human-like self-training, and have become one of the most concerned fields. In recent years, there has also been an increasing discussion of artificial intelligence algorithms. How AI algorithms are protected has also become a matter of great concern for many companies and creators. In this context, this paper will take artificial intelligence algorithms as the research object to carry out the paper. This paper will introduce the legal nature of algorithms, reveal the introduction and ethical dilemma of the existing artificial intelligence algorithm rights ownership dilemma of artificial intelligence algorithm intellectual property protection, and put forward feasible suggestions for the construction of China's artificial intelligence algorithm intellectual property system. It should be made clear that China's existing patent protection path cannot fully respond to all the needs that artificial intelligence algorithms want to be protected, so some adjustments need to be made to the patent law. First of all, it should be clarified which artificial intelligence algorithms can and cannot become the object of patent authorization. As a new thing, artificial intelligence algorithms are different from patents that are obviously novel, practical and inventive in the traditional sense. In order to better protect artificial intelligence algorithms and promote the better development of artificial intelligence algorithm technology, in view of these special differences, certain adjustments have been made to the examination standards of China's patent substantive elements according to the characteristics of patentable artificial intelligence algorithm objects.

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Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (2020): 31–44. https://doi.org/10.5281/zenodo.3690787.

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In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.

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Xu, Minghai, Li Cao, Dongwan Lu, Zhongyi Hu, and Yinggao Yue. "Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization." Biomimetics 8, no. 2 (2023): 235. http://dx.doi.org/10.3390/biomimetics8020235.

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Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.

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Thanoon, Radhwan Basim. "Hybrid Inverse Weed Optimization Algorithm with Math-Flame Optimization Algorithm." sinkron 8, no. 3 (2024): 2008–21. http://dx.doi.org/10.33395/sinkron.v8i3.13755.

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In this work, two Meta-Heuristic Algorithms were hybridized, the first is the Invers Weed optimization algorithm (IWO), which is a passing multiple algorithm, and the second is the Moth-flame Optimization Algorithm (MFO). Which depend in their behavior on the intelligence of the swarm and the intelligence of society, and they have unique characteristics that exceed the characteristics of the intelligence of other swarms because they are efficient in achieving the right balance between exploration and exploitation. So the new algorithm improves the initial population that is randomly generated, A process of hybridization was made between the IWO and MFO Algorithm to call The new hybrid algorithm (IWOMFO). The new hybrid algorithm was used for 16 high-scaling optimization functions with different community sizes and 250 repetitions. The Algorithm showed access to optimal solutions by achieving the value Minority () for most of these functions and the results of this algorithm are compared with the basic algorithms IWO, MFO

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Dissertations / Theses on the topic "Intelligence algorithm"

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Ray, Katrina. "Algorithm capability and applications in artificial intelligence." Thesis, Connect to title online (Scholars' Bank) Connect to title online (ProQuest), 2008. http://hdl.handle.net/1794/9504.

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Vaddhireddy, Jyothirmye. "A Novel Swarm Intelligence based IWD Algorithm for Routing in MANETs." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1321589580.

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Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.

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Cette thèse porte sur la configurationAutomatisée des algorithmes qui vise à trouver le meilleur paramétrage à un problème donné ou une catégorie deproblèmes.Le problème de configuration de l'algorithme revient doncà un problème de métaFoptimisation dans l'espace desparamètres, dont le métaFobjectif est la mesure deperformance de l’algorithme donné avec une configuration de paramètres donnée.Des approches plus récentes reposent sur une description des problèmes et ont pour but d’apprendre la relationentre l’espace des caractéristiques des problèmes etl’espace des configurations de l’algorithme à paramétrer.Cette thèse de doctorat porter le CAPI (Configurationd'Algorithme Par Instance) pour résoudre des problèmesd'optimisation de boîte noire continus, où seul un budgetlimité d'évaluations de fonctions est disponible. Nous étudions d'abord' les algorithmes évolutionnairesPour l'optimisation continue, en mettant l'accent sur deux algorithmes que nous avons utilisés comme algorithmecible pour CAPI,DE et CMAFES.Ensuite, nous passons en revue l'état de l'art desapproches de configuration d'algorithme, et lesdifférentes fonctionnalités qui ont été proposées dansla littérature pour décrire les problèmesd'optimisation de boîte noire continue.Nous introduisons ensuite une méthodologie générale Pour étudier empiriquement le CAPI pour le domainecontinu, de sorte que toutes les composantes du CAPIpuissent être explorées dans des conditions réelles.À cette fin, nous introduisons également un nouveau Banc d'essai de boîte noire continue, distinct ducélèbre benchmark BBOB, qui est composé deplusieurs fonctions de test multidimensionnelles avec'différentes propriétés problématiques, issues de lalittérature.La méthodologie proposée est finalement appliquée 'àdeux AES. La méthodologie est ainsi, validéempiriquement sur le nouveau banc d’essaid’optimisation boîte noire pour des dimensions allant jusqu’à 100<br>This PhD thesis focuses on the automated algorithm configuration that aims at finding the best parameter setting for a given problem or a' class of problem. The Algorithm Configuration problem thus amounts to a metal Foptimization problem in the space of parameters, whosemetaFobjective is the performance measure of the given algorithm at hand with a given parameter configuration. However, in the continuous domain, such method can only be empirically assessed at the cost of running the algorithm on some problem instances. More recent approaches rely on a description of problems in some features space, and try to learn a mapping from this feature space onto the space of parameter configurations of the algorithm at hand. Along these lines, this PhD thesis focuses on the Per Instance Algorithm Configuration (PIAC) for solving continuous black boxoptimization problems, where only a limited budget confessionnalisations available. We first survey Evolutionary Algorithms for continuous optimization, with a focus on two algorithms that we have used as target algorithm for PIAC, DE and CMAFES. Next, we review the state of the art of Algorithm Configuration approaches, and the different features that have been proposed in the literature to describe continuous black box optimization problems. We then introduce a general methodology to empirically study PIAC for the continuous domain, so that all the components of PIAC can be explored in real Fworld conditions. To this end, we also introduce a new continuous black box test bench, distinct from the famous BBOB'benchmark, that is composed of a several multiFdimensional test functions with different problem properties, gathered from the literature. The methodology is finally applied to two EAS. First we use Differential Evolution as'target algorithm, and explore all the components of PIAC, such that we empirically assess the best. Second, based on the results on DE, we empirically investigate PIAC with Covariance Matrix Adaptation Evolution Strategy (CMAFES) as target algorithm. Both use cases empirically validate the proposed methodology on the new black box testbench for dimensions up to100

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Coletti, Mark. "An analysis of a model-based evolutionary algorithm| Learnable Evolution Model." Thesis, George Mason University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3625081.

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<p>An evolutionary algorithm (EA) is a biologically inspired metaheuristic that uses mutation, crossover, reproduction, and selection operators to evolve solutions for a given problem. Learnable Evolution Model (LEM) is an EA that has an evolutionary algorithm component that works in tandem with a machine learner to collaboratively create populations of individuals. The machine learner infers rules from best and least fit individuals, and then this knowledge is exploited to improve the quality of offspring. </p><p> Unfortunately, most of the extant work on LEM has been <i>ad hoc </i>, and so there does not exist a deep understanding of how LEM works. And this lack of understanding, in turn, means that there is no set of best practices for implementing LEM. For example, most LEM implementations use rules that describe value ranges corresponding to areas of higher fitness in which offspring should be created. However, we do not know the efficacy of different approaches for sampling those intervals. Also, we do not have sufficient guidance for assembling training sets of positive and negative examples from populations from which the ML component can learn. </p><p> This research addresses those open issues by exploring three different rule interval sampling approaches as well as three different training set configurations on a number of test problems that are representative of the types of problems that practitioners may encounter. Using the machine learner to create offspring induces a unique emergent selection pressure separate from the selection pressure that manifests from parent and survivor selection; an outcome of this research is a partially ordered set of the impact that these rule interval sampling approaches and training set configurations have on this selection pressure that practitioners can use for implementation guidance. That is, a practitioner can modulate selection pressure by traversing a set of design configurations within a Hasse graph defined by partially ordered selection pressure. </p>

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Nettleton, David John. "Evolutionary algorithms in artificial intelligence : a comparative study through applications." Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5951/.

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For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system.

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Wilke, Daniel N. "Analysis of the particle swarm optimization algorithm." Pretoria : [s.n.], 2005. http://upetd.up.ac.za/thesis/available/etd-01312006-125743.

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Machado, Beatriz. "Artificial intelligence to model bedrock depth uncertainty." Thesis, KTH, Jord- och bergmekanik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252317.

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The estimation of bedrock level for soil and rock engineering is a challenge associated to many uncertainties. Nowadays, this estimation is performed by geotechnical or geophysics investigations. These methods are expensive techniques, that normally are not fully used because of limited budget. Hence, the bedrock levels in between investigations are roughly estimated and the uncertainty is almost unknown. Machine learning (ML) is an artificial intelligence technique that uses algorithms and statistical models to predict determined tasks. These mathematical models are built dividing the data between training, testing and validation samples so the algorithm improve automatically based on passed experiences. This thesis explores the possibility of applying ML to estimate the bedrock levels and tries to find a suitable algorithm for the prediction and estimation of the uncertainties. Many diferent algorithms were tested during the process and the accuracy level was analysed comparing with the input data and also with interpolation methods, like Kriging. The results show that Kriging method is capable of predicting the bedrock surface with considerably good accuracy. However, when is necessary to estimate the prediction interval (PI), Kriging presents a high standard deviation. The machine learning presents a bedrock surface almost as smooth as Kriging with better results for PI. The Bagging regressor with decision tree was the algorithm more capable of predicting an accurate bedrock surface and narrow PI.<br>BIG and BeFo project "Rock and ground water including artificial intelligence

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Topalli, Ayca Kumluca. "Hybrid Learning Algorithm For Intelligent Short-term Load Forecasting." Phd thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/627505/index.pdf.

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Short-term load forecasting (STLF) is an important part of the power generation process. For years, it has been achieved by traditional approaches stochastic like time series<br>but, new methods based on artificial intelligence emerged recently in literature and started to replace the old ones in the industry. In order to follow the latest developments and to have a modern system, it is aimed to make a research on STLF in Turkey, by neural networks. For this purpose, a method is proposed to forecast Turkey&rsquo<br>s total electric load one day in advance. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to make use of the available past data for adapting the weights and to further adjust these connections according to the changing conditions. It is also suggested to tune the step size iteratively for better accuracy. Since a single neural network model cannot cover all load types, data are clustered due to the differences in their characteristics. Apart from this, special days are extracted from the normal training sets and handled separately. In this way, a solution is proposed for all load types, including working days, weekends and special holidays. For the selection of input parameters, a technique based on principal component analysis is suggested. A traditional ARMA model is constructed for the same data as a benchmark and results are compared. Proposed method gives lower percent errors all the time, especially for holiday loads. The average error for year 2002 data is obtained as 1.60%.

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Harrison, Kyle Robert. "An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm." Thesis, University of Pretoria, 2018. http://hdl.handle.net/2263/66103.

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The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phase. Self-adaptive algorithms eliminate the need to tune parameters in advance, while also providing real-time behaviour adaptation based on the current problem. This thesis first provides an in-depth review of existing self-adaptive particle swarm optimization (SAPSO) techniques. Their ability to attain order-2 stability is examined and it is shown that a majority of the existing SAPSO algorithms are guaranteed to exhibit either premature convergence or rapid divergence. A further investigation focusing on inertia weight control strategies demonstrates that none of the examined techniques outperform a static value. This thesis then investigates the performance of a wide variety of PSO parameter configurations, thereby discovering regions in parameter space that lead to good performance. This investigation provides strong empirical evidence that the best values to employ for the PSO control parameters change over time. Finally, this thesis proposes novel PSO variants inspired by results of the aforementioned studies.<br>Thesis (PhD)--University of Pretoria, 2018.<br>Computer Science<br>PhD<br>Unrestricted

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Cully, Antoine. "Creative Adaptation through Learning." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066664/document.

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Les robots ont profondément transformé l’industrie manufacturière et sont susceptibles de délivrer de grands bénéfices pour la société, par exemple en intervenant sur des lieux de catastrophes naturelles, lors de secours à la personne ou dans le cadre de la santé et des transports. Ce sont aussi des outils précieux pour la recherche scientifique, comme pour l’exploration des planètes ou des fonds marins. L’un des obstacles majeurs à leur utilisation en dehors des environnements parfaitement contrôlés des usines ou des laboratoires, est leur fragilité. Alors que les animaux peuvent rapidement s’adapter à des blessures, les robots actuels ont des difficultés à faire preuve de créativité lorsqu’ils doivent surmonter un problème inattendu: ils sont limités aux capteurs qu’ils embarquent et ne peuvent diagnostiquer que les situations qui ont été anticipées par leur concepteurs. Dans cette thèse, nous proposons une approche différente qui consiste à laisser le robot apprendre de lui-même un comportement palliant la panne. Cependant, les méthodes actuelles d’apprentissage sont lentes même lorsque l’espace de recherche est petit et contraint. Pour surmonter cette limitation et permettre une adaptation rapide et créative, nous combinons la créativité des algorithmes évolutionnistes avec la rapidité des algorithmes de recherche de politique à travers trois contributions : les répertoires comportementaux, l’adaptation aux dommages et le transfert de connaissance entre plusieurs tâches. D’une manière générale, ces travaux visent à apporter les fondations algorithmiques permettant aux robots physiques d’être plus robustes, performants et autonomes<br>Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, for example in search and rescue, disaster response, health care, and transportation. They are also invaluable tools for scientific exploration of distant planets or deep oceans. A major obstacle to their widespread adoption in more complex environments and outside of factories is their fragility. While animals can quickly adapt to injuries, current robots cannot “think outside the box” to find a compensatory behavior when they are damaged: they are limited to their pre-specified self-sensing abilities, which can diagnose only anticipated failure modes and strongly increase the overall complexity of the robot. In this thesis, we propose a different approach that considers having robots learn appropriate behaviors in response to damage. However, current learning techniques are slow even with small, constrained search spaces. To allow fast and creative adaptation, we combine the creativity of evolutionary algorithms with the learning speed of policy search algorithms through three contributions: the behavioral repertoires, the damage recovery using these repertoires and the transfer of knowledge across tasks. Globally, this work aims to provide the algorithmic foundations that will allow physical robots to be more robust, effective and autonomous

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Books on the topic "Intelligence algorithm"

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Silhavy, Radek, and Petr Silhavy, eds. Artificial Intelligence Algorithm Design for Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70518-2.

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Thomas, Abraham. The intuitive algorithm. Affiliated East-West Press, 1991.

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Shi, Yuhui. Emerging research on swarm intelligence and algorithm optimization. Information Science Reference, 2015.

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Falkenhainer, Brian. The structure-mapping engine: Algorithm and examples. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Thomas, Abraham. The intuitive algorithm: About artificial intelligence, the mind, and happiness. East West Books, 2004.

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Hedgley, David R. A formal algorithm for routing traces on a printed circuit board. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1996.

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Kacprzyk, Janusz. Music-Inspired Harmony Search Algorithm: Theory and Applications. Springer Berlin Heidelberg, 2009.

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Miranker, Daniel. TREAT: A new and efficient match algorithm for Al production systems. Pitman, 1990.

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Miranker, Daniel P. TREAT: A new and efficient match algorithm for Al production systems. Pitman, 1990.

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Miranker, Daniel P. TREAT: A new and efficient match algorithm for AI production systems. Pitman, 1990.

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Book chapters on the topic "Intelligence algorithm"

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Hedjazi, Seyyed Mahdi, and Samane Sadat Marjani. "Pruned Genetic Algorithm." In Artificial Intelligence and Computational Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16527-6_25.

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Yang, Xin-She, and Adam Slowik. "Firefly Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-13.

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Yang, Xin-She, and Adam Slowik. "Bat Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-4.

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Mirjalili, Seyedali. "Genetic Algorithm." In Studies in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93025-1_4.

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Boer, Alexander, Léon de Beer, and Frank van Praat. "Algorithm Assurance: Auditing Applications of Artificial Intelligence." In Progress in IS. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11089-4_7.

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AbstractAlgorithm assurance is a specific form of IT assurance that supports risk management and control on applications of risky algorithms in products and in organizations. These algorithms will often be characterized in organizations as applications of Artificial Intelligence (AI), as advanced analytics, or—simply—as predictive models. The aim of this chapter is to introduce the concept of algorithm assurance, to give some background on the relevance and importance of algorithm assurance, and to prepare the auditor for the basic skills needed to organize and execute an algorithm audit. In this chapter we will introduce the algorithm assurance engagement as a specific type of IT audit. After a general discussion of the background of algorithm assurance and the type of IT applications we are concerned with in this type of engagement, we will extensively discuss the scope of an algorithm assurance engagement, how to approach the risk assessment that should take place initially, how to set up and audit plan, and the audit techniques and tools that play a role in an audit plan.

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Łukasik, Szymon. "Grasshopper Optimization Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-15.

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Erdal, Ferhat, and Osman Tunca. "Hunting Search Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-17.

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Kashani, Ali R., Charles V. Camp, Hamed Tohidi, and Adam Slowik. "Krill Herd Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-18.

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Kashani, Ali R., Charles V. Camp, Moein Armanfar, and Adam Slowik. "Whale Optimization Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-24.

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Slowik, Adam, and Dorin Moldovan. "Crow Search Algorithm." In Swarm Intelligence Algorithms. CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-8.

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Conference papers on the topic "Intelligence algorithm"

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Li, Meixia. "Artificial Intelligence Algorithm Traceability." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022226.

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Gao, Fengxi, Zhuohang Wu, Qixiang Wang, et al. "Research on intelligent transformation of power grid based on artificial intelligence algorithm." In 2024 6th International Conference on Wireless Communications and Smart Grid, edited by Jinsong Wu and Pascal Lorenz. SPIE, 2024. http://dx.doi.org/10.1117/12.3049399.

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Wu, Mingze. "E-commerce Platform Product Recommendation Algorithm Combining SVM Algorithm and Attention Mechanism." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721960.

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Ivanov, Aleksandar. "Algorithm Development Using Artificial Intelligence: An Overview." In 2024 23rd International Symposium on Electrical Apparatus and Technologies (SIELA). IEEE, 2024. http://dx.doi.org/10.1109/siela61056.2024.10637807.

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Gao, Yuanyuan. "Intelligent Intelligence Analysis Algorithm Based on Regression Analysis." In 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2024. http://dx.doi.org/10.1109/icdcece60827.2024.10548537.

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Eggensperger, Katharina, Marius Lindauer, and Frank Hutter. "Neural Networks for Predicting Algorithm Runtime Distributions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/200.

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Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance. Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts. Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs. To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations. In an empirical study involving five algorithms for SAT solving and AI planning, we show that neural networks predict the true RTDs of unseen instances better than previous methods, and can even do so when only few runtime observations are available per training instance.

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Degroote, Hans. "Online Algorithm Selection." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/746.

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Algorithm selection approaches have achieved impressive performance improvements in many areas of AI. Most of the literature considers the offline algorithm selection problem, where the initial selection model is never updated after training. However, new data from running algorithms on instances becomes available while an algorithm selection method is in use. In this extended abstract, the online algorithm selection problem is considered. In online algorithm selection, additional data can be processed, and the selection model can change over time. This abstract details the online algorithm setting, shows that it is a contextual multi-armed bandit, proposes a solution methodology, and empirically validates it.

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Tran, Tan D., Canh V. Pham, Dung T. K. Ha, and Phuong N. H. Pham. "Improved Parallel Algorithm for Non-Monotone Submodular Maximization under Knapsack Constraint." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/217.

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This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm improves the best approximation factor of the existing parallel one from 8 to 7 with O(log n) adaptive complexity. The key idea of our approach is to create an alternate threshold algorithmic framework. This new strategy alternately constructs two disjoint candidate solutions within a constant number of sequence rounds. Then, the algorithm boosts solution quality without sacrificing the adaptive complexity. Extensive experimental studies on three applications, Revenue Maximization, Image Summarization, and Maximum Weighted Cut, show that our algorithm not only significantly increases solution quality but also requires comparative adaptivity to state-of-the-art algorithms.

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Wu, Xingyu, Yan Zhong, Jibin Wu, Bingbing Jiang, and Kay Chen Tan. "Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/579.

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Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution. Mainstream algorithm selection techniques heavily rely on problem features, while the role of algorithm features remains largely unexplored. Due to the intrinsic complexity of algorithms, effective methods for universally extracting algorithm information are lacking. This paper takes a significant step towards bridging this gap by introducing Large Language Models (LLMs) into algorithm selection for the first time. By comprehending the code text, LLM not only captures the structural and semantic aspects of the algorithm, but also demonstrates contextual awareness and library function understanding. The high-dimensional algorithm representation extracted by LLM, after undergoing a feature selection module, is combined with the problem representation and passed to the similarity calculation module. The selected algorithm is determined by the matching degree between a given problem and different algorithms. Extensive experiments validate the performance superiority of the proposed model and the efficacy of each key module. Furthermore, we present a theoretical upper bound on model complexity, showcasing the influence of algorithm representation and feature selection modules. This provides valuable theoretical guidance for the practical implementation of our method.

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Deng, Zhengyan. "Research on Intelligent Translation System Based on Artificial Intelligence Algorithm." In 2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT). IEEE, 2024. http://dx.doi.org/10.1109/iccect60629.2024.10545794.

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Reports on the topic "Intelligence algorithm"

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Asker, John, Chaim Fershtman, and Ariel Pakes. Artificial Intelligence and Pricing: The Impact of Algorithm Design. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28535.

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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancements included updates related to supporting agent training, leveraging high-performance computing resources, and developing infrastructure to support AI versus AI agent training and gameplay. After evaluating agent training across different algorithm options, Deep Q-Network–trained agents performed better compared to those trained with Advantage Actor Critic or Proximal Policy Optimization algorithms. Experimentation in varying scenarios revealed acceptable performance from agents trained in the original baseline scenario. By training a blue agent against a previously trained red agent, researchers successfully demonstrated the AI versus AI training and gameplay capability. Observing results from agent gameplay revealed the emergence of behavior indicative of two principles of war, which were economy of force and mass.

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Liao, Han. New heuristic algorithm to improve the Minimax for Gomoku artificial intelligence. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1052.

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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristics as race, gender and class. Second, among the many sources of bias in military AI, three broad categories are prominent: bias in society; bias in data processing and algorithm development; and bias in use. Third, bias in military AI can have various humanitarian consequences depending on context and use. These range from misidentifying people and objects in targeting decisions to generating flawed assessments of humanitarian needs.

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Stone, D. K. Comprehensive Angular Response Study of LLNL Panasonic Dosimeter Configurations and Artificial Intelligence Algorithm. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1373656.

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Movellan, J. R., and J. L. McClelland. Learn Probability Distributions with The Contrastive Hebbian Algorithm. The Artificial Intelligence and Psychology Project. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242210.

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Stone, Daniel K. Comprehensive Angular Response Study of LLNL Panasonic Dosimeter Configurations and Artificial Intelligence Algorithm (Rev. 1). Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1529829.

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Lewis, Dustin, ed. A Compilation of Materials Apparently Reflective of States’ Views on International Legal Issues pertaining to the Use of Algorithmic and Data-reliant Socio-technical Systems in Armed Conflict. Harvard Law School Program on International Law and Armed Conflict, 2020. http://dx.doi.org/10.54813/cawz3627.

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This document is a compilation of materials that at least appear to be reflective of one or more states’ views on international legal issues pertaining to the actual or possible use of algorithmic and data-reliant socio-technical systems in armed conflict. In September of 2018, the Harvard Law School Program on International Law and Armed Conflict (HLS PILAC) commenced a project titled “International Legal and Policy Dimensions of War Algorithms: Enduring and Emerging Concerns.”[1] The project builds on the program’s earlier research and policy initiative on war-algorithm accountability. A goal of the current project is to help strengthen international debate and inform policymaking on the ways that artificial intelligence and complex computer algorithms are transforming war, as well as how international legal and policy frameworks already govern, and might further regulate, the design, development, and use of those technologies. The project is financially supported by the Ethics and Governance of Artificial Intelligence Fund. In creating this compilation, HLS PILAC seeks in part to provide a resource through which the positions of states with divergent positions on certain matters potentially of international public concern can be identified. Legal aspects of war technologies are more complex than some governments, scholars, and advocates allow. In the view of HLS PILAC, knowledge of the legal issues requires awareness of the multiple standpoints from which these arguments are fashioned. An assumption underlying how we approach these inquiries is that an assessment concerning international law in this area ought to take into account the perspectives of as many states (in addition to other relevant actors) as possible.

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Thegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Niño Bulan, Ron Lester Durante, and Jayzon Mag-atas. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys. Asian Development Bank, 2022. http://dx.doi.org/10.22617/wps220587-2.

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This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region. The paper notes that collecting information on road quality is difficult, particularly in harder to reach middle- and low-income areas, and explains why this method offers an alternative. It shows how the study’s preliminary algorithm was created using satellite imagery and existing road roughness data from the Philippines. It assesses the accuracy rate and finds it sufficient for the preliminary identification of poor to bad roads. It notes that additional enhancements are needed to increase its prediction accuracy and make it more robust.

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Osadchyi, Viacheslav V., Hanna B. Varina, Kateryna P. Osadcha, et al. The use of augmented reality technologies in the development of emotional intelligence of future specialists of socionomic professions under the conditions of adaptive learning. CEUR Workshop Proceedings, 2020. http://dx.doi.org/10.31812/123456789/4633.

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In modern conditions, innovative augmented reality technologies are actively developing, which are widespread in many areas of human activity. Introduction of advanced developments in the process of professional training of future specialists of socionomic professions in the conditions of adaptive training, contributes to the implementation of the principles of a personalized approach and increase the overall level of competitiveness. The relevant scientific article is devoted to the theoretical and empirical analysis result of conducting a psychodiagnostic study on an innovative computer complex HC-psychotest. of the features of the implementation of augmented reality technologies in the construct of traditional psychological and pedagogical support aimed at the development of emotional intelligence of the future specialist. The interdisciplinary approach was used while carrying out the research work at the expense of the general fund of the state budget: “Adaptive system for individualization and personalization of professional training of future specialists in the conditions of blended learning”. A comprehensive study of the implementation of traditional psychological-pedagogical and innovative augmented reality technologies was conducted in the framework of scientific cooperation of STEAM-Laboratory, Laboratory of Psychophysiological Research and Laboratory of Psychology of Health in Bogdan Khmelnitsky Melitopol State Pedagogical University. The theoretical analysis considers the structural model of emotional intelligence of the future specialist of socionomic professions, which is represented by two structural components: intrapersonal construct of emotional intelligence and interpersonal construct of emotional intelligence. Each component mediates the inherent emotional intelligence of interpretive, regulatory, adaptive, stress-protective and activating functions. The algorithm of the empirical block of research is presented by two stages: ascertaining and forming research. According to the results of the statement, low indicators were found on most scales, reflecting the general level of emotional intelligence development of future specialists, actualizing the need to find and implement effective measures for the development of emotional intelligence components in modern higher education and taking into account information development and digitalization. As part of the formative stage of the research implementation, a comprehensive program “Development of emotional intelligence of future professionals” was tested, which integrated traditional psychological and pedagogical technologies and innovative augmented reality technologies. This program is designed for 24 hours, 6 thematic classes of 4 hours. According to the results of a comprehensive ascertaining and shaping research, the effectiveness of the influence of augmented reality technologies on the general index of emotional intelligence is proved. The step-by-step model of integration of augmented reality components influencing the ability to analyze, understand and regulate emotional states into a complex program of emotional intelligence development is demonstrated. According to the results of the formative study, there is a dominance of high indicators of the following components: intrapersonal (50%), interpersonal (53.3%). Thus, we can say that intrapersonal and interpersonal emotional intelligence together involve the actualization of various cognitive processes and skills, and are related to each other. Empirical data were obtained as a

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