Search results

1

METHODS OF USING THE QUADRATIC ASSIGNMENT PROBLEM SOLUTION

100%

Kudelska I.

LogForum

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2012

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vol. 8

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issue 3

177-189

EN

Background: Background: Quadratic assignment problem (QAP) is one of the most interesting of combinatorial optimization. Was presented by Koopman and Beckamanna in 1957, as a mathematical model of the location of indivisible tasks. This problem belongs to the class NP-hard issues. This forces the application to the solution already approximate methods for tasks with a small size (over 30). Even though it is much harder than other combinatorial optimization problems, it enjoys wide interest because it models the important class of decision problems. Material and methods: The discussion was an artificial intelligence tool that allowed to solve the problem QAP, among others are: genetic algorithms, Tabu Search, Branch and Bound. Results and conclusions: QAP did not arise directly as a model for certain actions, but he found its application in many areas. Examples of applications of the problem is: arrangement of buildings on the campus of the university, layout design of electronic components in systems with large scale integration (VLSI), design a hospital, arrangement of keys on the keyboard.

2

USING GENETIC ALGORITHM IN DYNAMIC MODEL OF SPECULATIVE ATTACK

100%

Gawrońska-Nowak B., Grabowski W.

Equilibrium. Quarterly Journal of Economics and Economic Policy

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2016

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vol. 11

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issue 2

287-306

EN

Evolution of speculative attack models shows certain progress in developing the idea of the role of expectations in the crisis mechanism. Obstfeld (1996) defines expectations as fully exogenous. Morris and Shin (1998) treat the expectations as endogenous (with respect to noise), not devoting too much attention to information structure of the foreign exchange market. Dynamic approach proposed by Angeletos, Hellwig and Pavan (2006) offers more sophisticated assumption about learning process. It tries to reflect time-variant and complex nature of information. However, this model ignores many important details like a Central Bank cost function. Genetic algorithm allows to avoid problems connected with incorporating information and expectations into agent decision-making process to an extent. There are some similarities between the evolution in Nature and currency market performance. In our paper an assumption about rational agent behaviour in the efficient market is criticised and we present our version of the dynamic model of a speculative attack, in which we use a genetic algorithm (GA) to define decision-making process of the currency market agents. The results of our simulation seem to be in line with the theory and intuition. An advantage of our model is that it reflects reality in a quite complex way, i.e. level of noise changes in time (decreasing), there are different states of fundamentals (with “more sensitive” upper part of the scale), the number of inflowing agents can be low or high (due to different globalization phases, different capital flow phases, different uncertainty levels).

3

Application of Genetic Algorithms in Design of Public Transport Network

100%

Lesiak P., Bojarczak P.

Logistics and Transport

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2015

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vol. 26

75-82

EN

The paper presents possibilities of application of genetic algorithms in design of public transport network. Transportation tasks such as determination of optimal routes and timetable for means of transport belong to difficult complex optimization problems, therefore they cannot be solved using traditional search algorithms. It turns out that genetic algorithms can be very useful to solve these transportation problem.

4

Exchange Rates: Predictable but not Explainable? Data Mining with Leading Indicators and Technical Trading Rules

100%

Brandl B., Department of Government U. o. V.

Acta Universitatis Lodziensis. Folia Oeconomica

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2005

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vol. 192

EN

This paper presents a data mining approach to forecasting exchange rates. It is assumed that exchange rates are determined by both fundamental and technical factors. The balance of fundamental and technical factors varies for each exchange rate and frequency. It is difficult for forecasters to establish the relative relevance of different kinds of factors given this mixture; therefore the utilization of data mining algorithms is advantageous. The approach applied uses a genetic algorithm and neural networks. Out-of-sample forecasting results are illustrated for five exchange rates on different frequencies and it is shown that data mining is able to produce forecasts that perform well.

PL

W artykule przedstawiono proces eksploracji danych statystycznych w prognozowaniu kursów walutowych. Zakładamy, że kursy walutowe pozostają pod wpływem zarówno czynników o charakterze fundamentalnym, jak i czynników pozaekonomicznych. Równowaga pomiędzy tymi czynnikami różni się w zależności od rodzaju kursu walutowego i częstotliwości jego pomiaru. Prognostykom trudno jest ustalić względną siłę wpływu różnych czynników, stąd analiza polegająca na eksploracji danych ma określone zalety. W proponowanym podejściu wykorzystano algorytmy genetyczne i sztuczne sieci neuronowe. Przedstawiliśmy wyniki eksperymentów prognostycznych poza próbą statystyczną w odniesieniu do pięciu kursów walutowych, obserwowanych z różną częstotliwością. Pokazaliśmy, że metoda eksploracji danych może stanowić skuteczne narzędzie prognostyczne.

5

Filozofia ewolucji Charlesa Sandersa Peirce’a a współczesne problemy algorytmów ewolucyjnych

89%

Kłóś A.

Semina Scientiarum

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2017

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vol. 16

EN

The article challenges the view that the Neo-Darwinian theory of evolutionis sufficient to explain the ongoing evolution. The classical evolutionary algorithmsbased on that theory suffer from the loss of diversity, stagnation andpremature convergence. The author claims that the cosmetic changes of thosetools are not sufficient to overcome this situation and the change of overall theoreticalframework is required. The proposition of a semiotic theory of evolutioncreated by Charles Sanders Peirce is revealed as an alternative to the classicalModern Synthesis. This alternative model of evolution is implemented intwo kinds of evolutionary algorithms: P-EA and SEAM, which simulate evolutionby virtue of cooperation and symbiosis respectively. The new approach toalgorithms constructs shows significant benefits upon classical evolutionaryalgorithms in benchmark tests, which may support the original claim that theaccepted theory of evolution needs rethinking today.

6

Development of a Web Application for Dynamic Production Scheduling in Small and Medium Enterprises

88%

Kofjač D., Knaflič A., Kljajić M.

Organizacija

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2010

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vol. 43

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issue 3

125-135

EN

This article describes the development of a web-based dynamic job-shop scheduling system for small and medium enterprises. In large enterprises, scheduling is mainly performed with appropriate technology by human experts; many small and medium enterprises lack the resources to implement such a task. The main objective was to develop a cost-effective, efficient solution for job-shop scheduling in small and medium enterprises with an emphasis on accessibility, platform independence and ease of use. For these reasons, we decided to develop a web-based solution with the main emphasis on the development of an intelligent and dynamic user interface. The solution is built upon modular programming principles and enables dynamic scheduling on the basis of artificial intelligence, i.e. genetic algorithms. The solution has been developed as a standalone information system, which allows the management of virtually all scheduling activities through an administration panel. In addition, the solution covers the five main functionalities that completely support the scheduling process, i.e. making an inventory of resources available in the company, using it in the process of production planning, collecting data on production activities, distribution of up-to-date information and insight over events in the system.

7

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

88%

Korol T., Fotiadis A. K.

Oeconomia Copernicana

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2022

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vol. 13

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issue 2

407-438

EN

Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers? financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world.

8

Web Application for Hierarchical Organizational Structure Optimization – Human Resource Management Case Study

75%

Kofjač D., Bavec B., Škraba A.

Organizacija

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2015

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vol. 48

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issue 3

177-186

EN

Background and Purpose: In a complex strictly hierarchical organizational structure, undesired oscillations may occur, which have not yet been adequately addressed. Therefore, parameter values, which define fluctuations and transitions from one state to another, need to be optimized to prevent oscillations and to keep parameter values between lower and upper bounds. The objective was to develop a simulation model of hierarchical organizational structure as a web application to help in solving the aforementioned problem. Design/Methodology/Approach: The hierarchical structure was modeled according to the principles of System Dynamics. The problem of the undesired oscillatory behavior was addressed with deterministic finite automata, while the flow parameter values were optimized with genetic algorithms. These principles were implemented as a web application with JavaScript/ECMAScript. Results: Genetic algorithms were tested against well-known instances of problems for which the optimal analytical values were found. Deterministic finite automata was verified and validated via a three-state hierarchical organizational model, successfully preventing the oscillatory behavior of the structure. Conclusion: The results indicate that the hierarchical organizational model, genetic algorithms and deterministic finite automata have been successfully implemented with JavaScript as a web application that can be used on mobile devices. The objective of the paper was to optimize the flow parameter values in the hierarchical organizational model with genetic algorithms and finite automata. The web application was successfully used on a three-state hierarchical organizational structure, where the optimal flow parameter values were determined and undesired oscillatory behavior was prevented. Therefore, we have provided a decision support system for determination of quality restructuring strategies.

9

Evolutionary Schema of Modeling Based on Genetic Algorithms

75%

Stacewicz P.

Studies in Logic, Grammar and Rhetoric

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2015

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vol. 40

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issue 1

219-239

EN

In this paper, I propose a populational schema of modeling that consists of: (a) a linear AFSV schema (with four basic stages of abstraction, formalization, simplification, and verification), and (b) a higher-level schema employing the genetic algorithm (with partially random procedures of mutation, crossover, and selection). The basic ideas of the proposed solution are as follows: (1) whole populations of models are considered at subsequent stages of the modeling process, (2) successive populations are subjected to the activity of genetic operators and undergo selection procedures, (3) the basis for selection is the evaluation function of the genetic algorithm (this function corresponds to the model verification criterion and reflects the goal of the model). The schema can be applied to automate the modeling of the mind/brain by means of artificial neural networks: the structure of each network is modified by genetic operators, modified networks undergo a learning cycle, and successive populations of networks are verified during the selection procedure. The whole process can be automated only partially, because it is the researcher who defines the evaluation function of the genetic algorithm.

10

Multi-criterion optimisation of transport orders with the innovative evolutionary approach

63%

Bułat R., Radziszewski M.

Zeszyty Naukowe Małopolskiej Wyższej Szkoły Ekonomicznej w Tarnowie

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2011

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issue 1(17)

43-53

EN

One of the common problems encountered frequently in logistic issues is PDPTW (pickup and delivery problem with time windows) where a limited transport base is to be used to expedite goods in an efficient way from point A to point B. Every organisation, both business and non-profit is, for obvious reasons, unable to grasp the whole logistic process without the aid of automation, so it has to be equipped with a logistics support system. A viable alternative to other analytical solutions can therefore come in the form of a system based on genetic algorithms, which takes into account the limitations of the infrastructure, the time frame and the resulting penalty for any delay. This platform should also allow for the transition from a mathematically defined solution to a problem (however little practical use it has) to the real logistical problems based on the actual needs of the industry. Such a system was implemented, and with the basic genetic operators (cloning, mutation and crossover) is able to plan a solution for any arbitrarily defined, solvable problem of transportation, with the help of any algorithm using those operators. After starting the program and entering the dataset, the pre-set number of simulated generations of the genetic algorithm is started with the default chosen SPEA algorithm (strength Pareto evolutionary algorithm). The results of the simulation in the form of the final set of solutions are being saved to a file. For the algorithm applied to the test problem, the optimal solution for each variable, or middle-ground solutions were found.

PL

Jednym ze standardowych problemów spotykanych często w zagadnieniach logistycznych jest PDPTW (Pickup and Delivery Problem with Time Windows), gdzie dysponując ograniczoną bazą transportową, należy w sposób efektywny transportować towary z punktu A do B. Każda organizacja, zarówno biznesowa, jak i o charakterze niekomercyjnym, z oczywistych powodów niemożności ogarnięcia całościowo procesów logistycznych bez pomocy automatyzacji musi być wyposażona w system wsparcia logistycznego. Alternatywą dla innych rozwiązań analitycznych może być zatem system oparty na algorytmach genetycznych, biorący pod uwagę możliwości infrastruktury oraz ramy czasowe i wynikające z nich kary za opóźnienia. Platforma ta powinna też umożliwić przejście od rozwiązywania problematu zdefiniowanego matematycznie (jednak mającego nikłe zastosowanie praktyczne) do problemów logistycznych opartych na faktycznych potrzebach przemysłowych. System taki został zaimplementowany i przy użyciu podstawowych operatorów genetycznych – klonowania, mutacji i krzyżówki jest w stanie planować rozwiązania dla dowolnie zdefiniowanego rozwiązywalnego problemu transportowego oraz dowolnie zdefiniowanego algorytmu używającego tych operatorów. Po uruchomieniu programu i wprowadzeniu danych rozpoczynana jest symulacja zadanej ilości pokoleń algorytmu genetycznego, domyślnie wykonywanych według algorytmu SPEA (Strength Pareto Evolutionary Algorithm). Wyniki symulacji w postaci końcowego zbioru rozwiązań wypisywane są do pliku. Dla zastosowanego algorytmu dla problemu testowego znalezione zostały rozwiązania optymalne dla każdej ze zmiennych bądź rozwiązania pośrednie.

11

The Multicriteria Selection Methodology of the Decision Support System

63%

Patalas-Maliszewska J., Krupa T.

Foundations of Management

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2010

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vol. 2

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issue 2

51-62

EN

An issue of building a tool orientated to the support of selection process of decision support system (DSS) for the sector of small and medium enterprises is presented in the article. A group method of data handling (GMDH) whose application allows to objectives a search process of required DSS, with assumed costs and other existing resource limitations, is proposed for this purpose. The article is devoted to a problem of GMDH adjustment to solving the tasks related to the selection of a required system from among the information systems of DSS class, according to the criteria determined by a future user of this system. The GMDH takes into account among other things such assumptions as: a precise description of dependences between input and output data in a specified time horizon, independence of the user's knowledge (the values of observed data from the past registered in the base are the input data) and minimization of modelling errors (the selection of solutions takes place by means of a selected evaluation rule of quality estimation).

12

O użyteczności sieci neuronowych i algorytmów genetycznych w realizacji inwestycyjnych systemów decyzyjnych

51%

Morajda J.

Zeszyty Naukowe Małopolskiej Wyższej Szkoły Ekonomicznej w Tarnowie

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2006

|

issue 1(9)

217-230

PL

W artykule omówiono podstawowe aspekty realizacji aktywnych strategii inwestycyjnych na rynkach finansowych z wykorzystaniem systemów wspomagania decyzji (systemów transakcyjnych), w kontekście klasycznych teorii zarządzania portfelem inwestycyjnym. Wskazano zasadnicze przesłanki zastosowania metod sztucznej inteligencji, takich jak sieci neuronowe i algorytmy genetyczne, do konstrukcji inwestycyjnych systemów decyzyjnych. Przedstawiono charakterystykę sieci neuronowych oraz algorytmów genetycznych jako efektywnych narzędzi w modelowaniu i prognozowaniu rynków finansowych.

EN

The paper discusses basic aspects of application of active investment strategies in financial markets - in the context of classic theories of portfolio management. Such active strategies are generated with the use of decision support systems (transaction systems). The main assumptions of utilisation of artificial intelligence methods, such as neural networks and genetic algorithms, in the construction of investment decision systems have been indicated. The characteristic of neural networks and genetic algorithms as effective tools in financial markets modelling and prediction has also been discussed here.

Refine search results

METHODS OF USING THE QUADRATIC ASSIGNMENT PROBLEM SOLUTION

USING GENETIC ALGORITHM IN DYNAMIC MODEL OF SPECULATIVE ATTACK

Application of Genetic Algorithms in Design of Public Transport Network

Exchange Rates: Predictable but not Explainable? Data Mining with Leading Indicators and Technical Trading Rules

Filozofia ewolucji Charlesa Sandersa Peirce’a a współczesne problemy algorytmów ewolucyjnych

Development of a Web Application for Dynamic Production Scheduling in Small and Medium Enterprises

Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan

Web Application for Hierarchical Organizational Structure Optimization – Human Resource Management Case Study

Evolutionary Schema of Modeling Based on Genetic Algorithms

Multi-criterion optimisation of transport orders with the innovative evolutionary approach

The Multicriteria Selection Methodology of the Decision Support System

O użyteczności sieci neuronowych i algorytmów genetycznych w realizacji inwestycyjnych systemów decyzyjnych