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Wyświetlanie 1-15 z 43
Tytuł:
Multi-population-based algorithm with an exchange of training plans based on population evaluation
Autorzy:
Łapa, Krystian
Cpałka, Krzysztof
Kisiel-Dorohinicki, Marek
Paszkowski, Józef
Dębski, Maciej
Le, Van-Hung
Powiązania:
https://bibliotekanauki.pl/articles/2147148.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
population-based algorithm
multi-population algorithm
hybrid algorithm
island algorithm
subpopulation evaluation
training plan
Opis:
Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 239--253
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parallel PBIL applied to power system controller design
Autorzy:
Folly, K.
Powiązania:
https://bibliotekanauki.pl/articles/91747.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Population-Based Incremental Learning algorithm
PBIL algorithm
Opis:
Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 215-223
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minig rules of concept drift using genetic algorithm
Autorzy:
Vivekanandan, P.
Nedunchezhian, R.
Powiązania:
https://bibliotekanauki.pl/articles/91705.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic algorithm
CDR-tree algorithm
rules
data mining
Opis:
In a database the data concepts changes over time and this phenomenon is called as concept drift. Rules of concept drift describe how the concept changes and sometimes they are interesting and mining those rules becomes more important. CDR tree algorithm is currently used to identify the rules of concept drift. Building a CDR tree becomes a complex process when the domain values of the attributes get increased. Genetic Algorithms are traditionally used for data mining tasks. In this paper, a Genetic Algorithm based approach is proposed for mining the rules of concept drift, which makes the mining task simpler and accurate when compared with the CDR-tree algorithm.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 2; 135-145
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms
Autorzy:
Peteiro-Barral, D.
Guijarro-Berdiñas, B.
Pérez-Sánchez, B.
Powiązania:
https://bibliotekanauki.pl/articles/91888.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
artificial neural networks
genetic algorithm
Devonet algorithm
Opis:
It is a fact that traditional algorithms cannot look at a very large data set and plausibly find a good solution with reasonable requirements of computation (memory, time and communications). In this situation, distributed learning seems to be a promising line of research. It represents a natural manner for scaling up algorithms inasmuch as an increase of the amount of data can be compensated by an increase of the number of distributed locations in which the data is processed. Our contribution in this field is the algorithm Devonet, based on neural networks and genetic algorithms. It achieves fairly good performance but several limitations were reported in connection with its degradation in accuracy when working with heterogeneous data, i.e. the distribution of data is different among the locations. In this paper, we take into account this heterogeneity in order to propose several improvements of the algorithm, based on distributing the computation of the genetic algorithm. Results show a significative improvement of the performance of Devonet in terms of accuracy.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 5-20
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Parallel MCNN (PMCNN) with application to prototype selection on large and streaming data
Autorzy:
Devi, V. S.
Meena, L.
Powiązania:
https://bibliotekanauki.pl/articles/91686.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
prototype selection
one-pass algorithm
streaming data
distributed algorithm
Opis:
The Modified Condensed Nearest Neighbour (MCNN) algorithm for prototype selection is order-independent, unlike the Condensed Nearest Neighbour (CNN) algorithm. Though MCNN gives better performance, the time requirement is much higher than for CNN. To mitigate this, we propose a distributed approach called Parallel MCNN (pMCNN) which cuts down the time drastically while maintaining good performance. We have proposed two incremental algorithms using MCNN to carry out prototype selection on large and streaming data. The results of these algorithms using MCNN and pMCNN have been compared with an existing algorithm for streaming data.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 3; 155-169
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Clustering large-scale data based on modified affinity propagation algorithm
Autorzy:
Serdah, A. M.
Ashour, W. M.
Powiązania:
https://bibliotekanauki.pl/articles/91694.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
clustering
clustering algorithm
data clustering algorithm
propagation algorithm
Affinity Propagation
AP
klasteryzacja
algorytm klastrowania
algorytm propagacji
Opis:
Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 23-33
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary algorithm with a configurable search mechanism
Autorzy:
Łapa, Krystian
Cpałka, Krzysztof
Laskowski, Łukasz
Cader, Andrzej
Zeng, Zhigang
Powiązania:
https://bibliotekanauki.pl/articles/1837536.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
evolutionary algorithm
population-based algorithm
optimization
operator pool
operator selection
individual selection
Opis:
In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 3; 151-171
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Mosaic reasoning for discoveries
Autorzy:
Stilman, B.
Powiązania:
https://bibliotekanauki.pl/articles/91717.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
algorithm optimizing warfighting
Linguistic Geometry
LG
Algorithm of Discovery
DNA
mosaic
Opis:
We investigate structure of the Primary Language of the human brain as introduced by J. von Neumann in 1957. Two components have been investigated, the algorithm optimizing warfighting, Linguistic Geometry (LG), and the algorithm for inventing new algorithms, the Algorithm of Discovery. The latter is based on multiple thought experiments, which manifest themselves via mental visual streams (“mental movies”). There are Observation, Construction and Validation classes of streams. Several visual streams can run concurrently and exchange information between each other. The streams may initiate additional thought experiments, program them, and execute them in due course. The visual streams are focused employing the algorithm of “a child playing a construction set” that includes a visual model, a construction set, and the Ghost. Mosaic reasoning introduced in this paper is one of the major means to focusing visual streams in a desired direction. It uses analogy with an assembly of a picture of various colorful tiles, components of a construction set. In investigating role of mosaic reasoning in the Algorithm of Discovery, in this paper, I replay a series of four thought experiments related to the discovery of the structure of the molecule of DNA. Only the fourth experiment was successful. This series of experiments reveals how a sequence of failures eventually leads the Algorithm to a discovery. This series permits to expose the key components of the mosaic reasoning, tiles and aggregates, local and global matching rules, and unstructured environment. In particular, it reveals the aggregates and the rules that played critical role in the discovery of the structure of DNA. They include the generator and the plug-in aggregates, the transformation and complementarity matching rules, and the type of unstructured environment. For the first time, the Algorithm of Discovery has been applied to replaying discoveries not related to LG and even to mathematics.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 147-173
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Population diversity maintenance in brain storm optimization algorithm
Autorzy:
Cheng, S.
Shi, Y.
Qin, Q.
Zhang, Q
Bai, R.
Powiązania:
https://bibliotekanauki.pl/articles/91571.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
brainstorm
optimization algorithm
convergence
divergence
brainstorm optimization
BSO
swarm intelligence
BSO algorithm
Opis:
The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm’s exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 83-97
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the hybridization of the artificial Bee Colony and Particle Swarm Optimization Algorithms
Autorzy:
El-Abd, M.
Powiązania:
https://bibliotekanauki.pl/articles/91658.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Artificial Bee Colony Algorithm
ABC
particle swarm optimization (PSO)
PSO
hybridization
hybrid algorithm
CEC05
Opis:
In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one, where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Three different versions of the hybrid algorithm are tested in this work by experimenting with different selection mechanisms for the ABC component. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics, namely, the solution reached, the success rate, and the performance rate.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 2; 147-155
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
DSMK-means “density-based split-and-Merge K-means clustering algorithm
Autorzy:
Aldahdooh, R. T.
Ashour, W.
Powiązania:
https://bibliotekanauki.pl/articles/91719.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
clustering
K-means
Density-based Split
Merge K-means clustering Algorithm
DSMK-means
clustering algorithm
Opis:
Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. This paper introduces Density-based Split- and -Merge K-means clustering Algorithm (DSMK-means), which is developed to address stability problems of standard K-means clustering algorithm, and to improve the performance of clustering when dealing with datasets that contain clusters with different complex shapes and noise or outliers. Based on a set of many experiments, this paper concluded that developed algorithms “DSMK-means” are more capable of finding high accuracy results compared with other algorithms especially as they can process datasets containing clusters with different shapes, densities, or those with outliers and noise.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 51-71
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
Autorzy:
Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/2201329.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Local Levenberg-Marquardt algorithm for learning feedforwad neural networks
Autorzy:
Bilski, Jarosław
Kowalczyk, Bartosz
Marchlewska, Alina
Zurada, Jacek M.
Powiązania:
https://bibliotekanauki.pl/articles/1837415.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
optimization problem
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel variant of the salp swarm algorithm for engineering optimization
Autorzy:
Jia, Fuyun
Luo, Sheng
Yin, Guan
Ye, Yin
Powiązania:
https://bibliotekanauki.pl/articles/23944824.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
salp swarm algorithm
meta-heuristic algorithm
chaos theory
sine-cosine mechanism
quantum computation
optimization design of engineering
Opis:
There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 131--149
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Browser fingerprint coding methods increasing the effectiveness of user identification in the web traffic
Autorzy:
Gabryel, Marcin
Grzanek, Konrad
Hayashi, Yoichi
Powiązania:
https://bibliotekanauki.pl/articles/1837413.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
browser fingerprint
device fingerprint
LSH algorithm
autoencoder
Opis:
Web-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 243-253
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 43

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