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Wyświetlanie 1-15 z 38
Tytuł:
Adaptive approaches to parameter control in genetic algorithms and genetic programming
Autorzy:
Spalek, J.
Gregor, M.
Powiązania:
https://bibliotekanauki.pl/articles/117900.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
adaptive approach
genetic algorithms
genetic programming
Opis:
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks such as automated design of control systems, where the space of solutions is non-trivial and may contain discontinuities. Several adaptive mechanisms for control of the search algorithm's parameters are proposed, investigated and compared to each other. It is shown that the proposed mechanisms are useful in preventing the search from getting trapped in local extremes of the fitness landscape.
Źródło:
Applied Computer Science; 2011, 7, 1; 38-56
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive switching of mutation rate for genetic algorithms and genetic programming
Autorzy:
Spalek, J.
Gregor, M.
Powiązania:
https://bibliotekanauki.pl/articles/118223.pdf
Data publikacji:
2011
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
genetic algorithms
genetic programming
adaptive mechanism
Opis:
The paper concerns the application of Genetic Algorithms and Genetic Programming to complex tasks such as automated design of control systems, where the space of solutions is non-trivial and may contain discontinuities. An adaptive value-switching mechanism for mutation rate control is proposed. It is shown that the proposed mechanism is useful in preventing the search from getting trapped in local extremes of the fitness landscape.
Źródło:
Applied Computer Science; 2011, 7, 1; 30-37
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GPFIS - control : a genetic fuzzy system for control tasks
Autorzy:
Koshiyama, A. S.
Vellasco, M. M. B. R.
Tanscheit, R.
Powiązania:
https://bibliotekanauki.pl/articles/91648.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic fuzzy controler
GFC
genetic programming fuzzy inference system for control
GPFISControl
multigene genetic programming
inverted pendulum
Opis:
This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFISControl). It is based on MultiGene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFISControl are considered: the CartCentering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFISControl in relation to other GFCs found in the literature.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 3; 167-179
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
LCS and GP Approaches to Multiplexer’s Problem
Autorzy:
Wasielewska, K.
Bagiński, M.
Seredyński, F.
Powiązania:
https://bibliotekanauki.pl/articles/92967.pdf
Data publikacji:
2006
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
learning classifier system
genetic programming
multiplexer problem
Opis:
In this paper we present the use of learning classifier systems and genetic programming to solving multiplexer’s problem. The function of multiplexer is the popular apparatus of researches which is used to investigate the effectiveness of systems based on evolutionary algorithms. It turns out that the eXtended Classifier System (XCS) learns the problem of multiplexer effectively and Genetic Programming (GP) finds the form of function of multiplexer correctly.
Źródło:
Studia Informatica : systems and information technology; 2006, 1(7); 195-206
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Algorytm optymalizacji rozdrabniania nasion oleistych z wykorzystaniem programowania genetycznego
The process initial optimization of grinding oil seeds out using genetics programming
Autorzy:
Jankowski, M.
Tyszczuk, K.
Kopacz, S.
Powiązania:
https://bibliotekanauki.pl/articles/2070346.pdf
Data publikacji:
2009
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
rozdrabnianie
programowanie genetyczne
model
milling
genetic programming
Źródło:
Inżynieria i Aparatura Chemiczna; 2009, 2; 54-55
0368-0827
Pojawia się w:
Inżynieria i Aparatura Chemiczna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An analysis of the performance of genetic programming for realised volatility forecasting
Autorzy:
Yin, Z.
O’Sullivan, C.
Brabazon, A.
Powiązania:
https://bibliotekanauki.pl/articles/91765.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
realised volatility
genetic programming
high frequency data
Opis:
Traditionally, the volatility of daily returns in financial markets is modeled autoregressively using a time-series of lagged information. These autoregressive models exploit stylised empirical properties of volatility such as strong persistence, mean reversion and asymmetric dependence on lagged returns. While these methods can produce good forecasts, the approach is in essence atheoretical as it provides no insight into the nature of the causal factors and how they affect volatility. Many plausible explanatory variables relating market conditions and volatility have been identified in various studies but despite the volume of research, we lack a clear theoretical framework that links these factors together. This setting of a theory-weak environment suggests a useful role for powerful model induction methodologies such as Genetic Programming (GP). This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration (waiting time between trades) and implied volatility. The forecasting performance from the evolved GP models is found to be significantly better than those numbers of benchmark forecasting models drawn from the finance literature, namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model (SR). Given the practical importance of improved forecasting performance for realised volatility this result is of significance for practitioners in financial markets.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 3; 155-172
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hash function generation by means of Gene Expression Programming
Autorzy:
Varrette, S.
Muszyński, J.
Bouvry, P.
Powiązania:
https://bibliotekanauki.pl/articles/106148.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
hash function
Gene Expression Programming
Genetic Programming
GEPHashSearch
cryptography
Opis:
Cryptographic hash functions are fundamental primitives in modern cryptography and have many security applications (data integrity checking, cryptographic protocols, digital signatures, pseudo random number generators etc.). At the same time novel hash functions are designed (for instance in the framework of the SHA-3 contest organized by the National Institute of Standards and Technology (NIST)), the cryptanalysts exhibit a set of statistical metrics (propagation criterion, frequency analysis etc.) able to assert the quality of new proposals. Also, rules to design "good" hash functions are now known and are followed in every reasonable proposal of a new hash scheme. This article investigates the ways to build on this experiment and those metrics to generate automatically compression functions by means of Evolutionary Algorithms (EAs). Such functions are at the heart of the construction of iterative hash schemes and it is therefore crucial for them to hold good properties. Actually, the idea to use nature-inspired heuristics for the design of such cryptographic primitives is not new: this approach has been successfully applied in several previous works, typically using the Genetic Programming (GP) heuristic [1]. Here, we exploit a hybrid meta-heuristic for the evolutionary process called Gene Expression Programming (GEP) [2] that appeared far more efficient computationally speaking compared to the GP paradigm used in the previous papers. In this context, the GEPHashSearch framework is presented. As it is still a work in progress, this article focuses on the design aspects of this framework (individuals definitions, fitness objectives etc.) rather than on complete implementation details and validation results. Note that we propose to tackle the generation of compression functions as a multi-objective optimization problem in order to identify the Pareto front i.e. the set of non-dominated functions over the four fitness criteria considered. If this goal is not yet reached, the first experimental results in a mono-objective context are promising and open the perspective of fruitful contributions to the cryptographic community.
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 3; 37-53
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Embryonic Architecture with Built-in Self-test and GA Evolved Configuration Data
Autorzy:
Malhotra, Gayatri
Duraiswamy, Punithavathi
Kishore, J.K.
Powiązania:
https://bibliotekanauki.pl/articles/27311869.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
embryonic
BIST
Self-test
Genetic Algorithm
Cartesian Genetic Programming
Opis:
The embryonic architecture, which draws inspiration from the biological process of ontogeny, has built-in mechanisms for self-repair. The entire genome is stored in the embryonic cells, allowing the data to be replicated in healthy cells in the event of a single cell failure in the embryonic fabric. A specially designed genetic algorithm (GA) is used to evolve the configuration information for embryonic cells. Any failed embryonic cell must be indicated via the proposed Built-in Selftest (BIST) the module of the embryonic fabric. This paper recommends an effective centralized BIST design for a novel embryonic fabric. Every embryonic cell is scanned by the proposed BIST in case the self-test mode is activated. The centralized BIST design uses less hardware than if it were integrated into each embryonic cell. To reduce the size of the data, the genome or configuration data of each embryonic cell is decoded using Cartesian Genetic Programming (CGP). The GA is tested for the 1-bit adder and 2-bit comparator circuits that are implemented in the embryonic cell. Fault detection is possible at every function of the cell due to the BIST module’s design. The CGP format can also offer gate-level fault detection. Customized GA and BIST are combined with the novel embryonic architecture. In the embryonic cell, self-repair is accomplished via data scrubbing for transient errors.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 2; 211--217
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Kosynteza rozproszonych systemów wbudowanych metodą programowania genetycznego
Hardware/software Co-Synthesis of Distributed Embedded Systems Using Genetic Programming
Autorzy:
Deniziak, S.
Górski, A.
Powiązania:
https://bibliotekanauki.pl/articles/156174.pdf
Data publikacji:
2008
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
programowanie genetyczne
kosynteza
genetic programming
hardware-software codesign
Opis:
W pracy zaprezentowana jest nowa metoda kosyntezy systemów wbudowanych specyfikowanych za pomocą grafów zadań, bazująca na metodzie programowania genetycznego. Przedstawione są propozycje reprezentowania procesu konstrukcji takiego systemu w formie drzewa stanowiącego tzw. genotyp. Następnie na drodze ewolucji (krzyżowania, mutacji, selekcji) generowane są kolejne "pokolenia" drzew, konstruujących systemy o coraz lepszych parametrach. W odróżnieniu od tradycyjnego podejścia genetycznego w metodzie programowania genetycznego (DGP) operuje się nie bezpośrednio na cechach rozwiązania (czyli tzw. fenotypach) ale na genotypach odpowiadających za tworzenie rozwiązań o wskazanych cechach. Przedstawione wyniki wykonanych eksperymentów świadczą o dużych możliwościach metody DGP również w zakresie kosyntezy.
This work presents a novel approach to hardware-software co-synthesis of distributed embedded systems, based on the developmental genetic programming. Unlike other genetic approaches where chromosomes represent solutions, in our method chromosomes represent system construction procedures. Thus, not the system architecture but the co-synthesis process is evolved. Finally a tree describing a construction of the final solution is obtained. The optimization process will be illustrated with examples. According to our best knowledge it is the first DGP approach that deals with the hardware-software co-synthesis.
Źródło:
Pomiary Automatyka Kontrola; 2008, R. 54, nr 8, 8; 472-474
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Grammars in genetic programming
Autorzy:
Wieczorek, W.
Czech, Z.
Powiązania:
https://bibliotekanauki.pl/articles/205856.pdf
Data publikacji:
2000
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
algorytm genetyczny
gramatyka
genetic algorithms
grammars
strongly typed genetic programming
Opis:
The work consists of two parts. In the first part the idea of genetic programming is presented and the basic elements of a genetic programming system are described. In the second part, considering a selected example, we describe the results of investigations of the influence of program grammars on the efficiency of genetic programming.
Źródło:
Control and Cybernetics; 2000, 29, 4; 1019-1030
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic Strategies for Autonomous Virtual Characters
Autorzy:
Lach, E.
Powiązania:
https://bibliotekanauki.pl/articles/93027.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
genetic programming
task control
machine learning
virtual characters animation
Opis:
Technique presented in the paper concerns automatic generation of strategies controlling virtual characters’ behaviour. Virtual people are currently widely used in many applications, especially in computer games, films and educational systems. A lot of researches focus on creating intelligent characters capable of deciding about their actions. The fully acceptable solution has not yet been found. The paper presents the problem of generating strategies by means of modified genetic programming. A new Guide-Path technique is introduced.
Źródło:
Studia Informatica : systems and information technology; 2007, 2(9); 57-68
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Enhancing island model genetic programming by controlling frequent trees
Autorzy:
Ono, Keiko
Hanada, Yoshiko
Kumano, Masahito
Kimura, Masahiro
Powiązania:
https://bibliotekanauki.pl/articles/91860.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
genetic programming
island model
frequent tree-based migration strategy
Opis:
In evolutionary computation approaches such as genetic programming (GP), preventing premature convergence to local minima is known to improve performance. As with other evolutionary computation methods, it can be difficult to construct an effective search bias in GP that avoids local minima. In particular, it is difficult to determine which features are the most suitable for the search bias, because GP solutions are expressed in terms of trees and have multiple features. A common approach intended to local minima is known as the Island Model. This model generates multiple populations to encourage a global search and enhance genetic diversity. To improve the Island Model in the framework of GP, we propose a novel technique using a migration strategy based on textit frequent trees and a local search, where the frequent trees refer to subtrees that appear multiple times among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of the fitness value and how many types of frequent trees have been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of frequent tree information into a migration strategy and local search effectively improves performance. The proposed method is shown to significantly outperform both a typical Island Model GP and the aged layered population structure method.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 51-65
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary prediction of manufacturing costs in tool manufacturing
Autorzy:
Ficko, M.
Vaupotič, B.
Balič, J.
Powiązania:
https://bibliotekanauki.pl/articles/384509.pdf
Data publikacji:
2007
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
prediction of tool manufacturing costs
case-based reasoning
genetic programming
Opis:
One of the most important factors in the offer for tool manufacture is the total manufacturing cost. Although the total manufacturing costs can be rather precisely determined by the cost analysis, this approach is not well applicable in tool-making due to cost and, particularly, time demand. Therefore, the authors propose a new approach to prediction of total manufacturing costs, which is based on case based-reasoning method and imitates the human expert. The system first abstracts from CAD-models the geometrical features, and then it calculates the similarities between the source cases and target case. The most similar cases are used for preparation of prediction by genetic programming. The genetic programming method provides the model connecting the individual geometrical features with the costs searched for. Regarding to the connections between geometrical features and tool cost of source cases the formula for calculation of tool cost of target case is being made. The experimental results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2007, 1, 4; 51-58
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolutionary data driven modelling and many objective optimization of non linear noisy data in the blast furnace iron making process
Autorzy:
Mahanta, Bashista Kumar
Chakraborti, Nirupam
Powiązania:
https://bibliotekanauki.pl/articles/29520226.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
reference vector
neural net
genetic programming
blast furnace
Opis:
Optimization of process parameters in modern blast furnace operation, where both control and accessing large data set with multiple variables and objectives is a challenging task. To handle such non-linear and noisy data set deep learning techniques have been used in recent time. In this study an evolutionary deep neural network algorithm (EvoDN2) has been applied to derive a data driven model for blast furnace. The optimal front generated from deep neural network is compared against the optimal models developed from bi-objective genetic programming algorithm (BioGP) and evolutionary neural network (EvoNN). The optimization process is applied to all the training models by using constraint based reference vector evolutionary algorithm (cRVEA).
Źródło:
Computer Methods in Materials Science; 2021, 21, 3; 163-175
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computational intelligence for predicting biological effects of drug absorption in lungs
Autorzy:
Pacławski, Adam
Szlęk, Jakub
Mendyk, Aleksander
Powiązania:
https://bibliotekanauki.pl/articles/305803.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
empirical model
absorption enhancers
pulmonary drugs
genetic programming
symbolic regression
computational intelligence
Opis:
Recently, the lungs have been extensively examined as a route for delivering drugs (active pharmaceutical ingredients, APIs) into the bloodstream; this is mainly due to the possibility of the noninvasive administration of macromolecules such as proteins and peptides. The absorption mechanisms of chemical compounds in the lungs are still not fully understood, which makes pulmonary formulation composition development challenging. This manuscript presents the development of an empirical model capable of predicting the excipients’ influence on the absorption of drugs in the lungs. Due to the complexity of the problem and the not-fully-understood mechanisms of absorption, computational intelligence tools were applied. As a result, a mathematical formula was established and analyzed. The normalized root-mean-squared error (NRMSE) and R2 of the model were 4.57%, and 0.83, respectively. The presented approach is beneficial both practically by developing an in silico predictive model and theoretically by gaining knowledge of the influence of APIs and excipient structure on absorption in the lungs.
Źródło:
Computer Science; 2019, 20 (1); 99-121
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 38

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