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Our project uses ideas first presented by Alan Turing. Turing's immense contribution to mathematics and computer science is widely known, but his pioneering work in artificial intelligence is relatively unknown. In the late 1940s Turing introduced discrete Boolean artificial neural networks and, it has been argued that, he suggested that these networks be trained via evolutionary algorithms. Both artificial neural networks and evolutionary algorithms are active fields of research. Turing's networks are very basic yet capable of complex tasks such as processing sequential input; consequently, they are an excellent model for investigating the application of evolutionary algorithms to artificial neural networks. We define an example of these networks using sequential input and output, and we devise evolutionary algorithms that train these networks. Our networks are discrete Boolean networks where every 'neuron' either performs NAND or identity, and they can represent any function that maps one sequence of bit strings to another. Our algorithms use supervised learning to discover networks that represent such functions. That is, when searching for a network that represents a particular function our algorithms use input-output pairs of that function as examples to aid the discovery of solution networks. To test our ideas we encode our networks and implement the algorithms in a computer program. Using this program we investigate the performance of our networks and algorithms on simple problems such as searching for networks that realize the parity function and the multiplexer function. This investigation includes the construction and testing of an intricate crossover operator. Because our networks are composed of simple 'neurons' they are a suitable test-bed for novel training schemes. To improve our evolutionary algorithms for some problems we employ the symmetry of the problem to reduce its search space. We devise and test a means of using subgroups of the group of permutation of inputs of a function to aid evolutionary searches search for networks that represent that function. In particular, we employ the action of the permutation group S₂ to 'cut down' the search space when we search for networks that represent functions such as parity.

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Prekiaujant vertybiniais popieriais, svarbiausia yra priimti teisingą sprendimą: pirkti arba parduoti. Daugelis investuotojų prieš priimdami sprendimą atkreipia dėmesį į pasirinktos akcijos kainos kitimo grafiką ir vadovaujasi juo. Tačiau ne kiekvienas investuotojas galėtų tiksliai apibūdinti savo pasirinktą grafinį modelį. Problemos aktualumas - Prognozuoti rinkas yra pakankamai sudėtinga, pastebimas žymus akcijų kursų svyravimas. Ženklūs akcijų kursų pasikeitimai skaičiuojami ne per metus ar mėnesius, o dienomis ar net valandomis. Investitoriams, finansų analitikams finansinėse rinkose sunku dirbti. Spekuliavimas akcijomis aktyviose akcijų rinkose yra labai rizikingas, bet pelningas užsiėmimas. Pasiūlius sprendimo priėmimo metodą investavimo procesas techniniu požiūriu supaprastės ir nereikalaus didelių sąnaudų, bei gilių žinių, leis platesniam ratui žmonių įeiti į akcijų rinką. Problema – Sudėtingas akcijų rinkų prognozavimas, kadangi pastebimas žymus akcijų kursų svyravimas, todėl rizikinga spekuliuoti akcijomis aktyviose akcijų rinkose. Baigiamojo darbo objektas – sprendimo priėmimo metodas finansinių rinkų prognozėms atlikti, remiantis neuroniniais tinklais ir spiečiaus algoritmu. Baigiamojo darbo tikslas – Spiečiaus intelekto taikymo finansų rinkose analizė ir optimizavimas. / One of the central problems in financial markets is to make the profitable stocks trading decisions using historical stocks' market data. This paper presents the decision-making methodology which is based on the application of neural networks and swarm intelligence technologies and is used to generate one-step ahead investment decisions. In brief, the proposed methodology draws from the analysis of historical stock prices variations. The variations are passed to neural networks and the recommendations for the next day are calculated. The stocks with the highest recommendations are considered for further experimental investigations. The core idea of this algorithm is to select three best neural networks for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market. The theme of the Master’s degree paper is “Analysis and Optimization of Swarm Intelligent in Financial Markets”. The object of the Master’s degree paper is decision making method for financial markets, re neural network and swarm intelligence.

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