Evolving neural networks for static single-position automated trading

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns

Goal generation with relevant and trusted beliefs

A rational agent adopts (or changes) its goals when new information (beliefs) becomes available or its desires (e.g., tasks it is supposed to carry out) change. In conventional approaches to goal generation in which a goal is considered as a \u201cparticular\u201d desire, a goal is adopted if and only if all conditions leading to its generation are satisfied. It is then supposed that all beliefs are equally relevant and their sources completely trusted. However, that is not a realistic setting. In fact, depending on the agent's trust in the source of a piece of information, an agent may decide how strongly it takes into consideration such piece of information in goal generation. On the other hand, not all beliefs are equally relevant to the adoption of a given goal, and a given belief may not be equally relevant to the adoption of different goals. We propose an approach which takes into account both the relevance of beliefs and the trust degree of the source from which the corresponding piece of information comes, in desire/goal generation. Two algorithms for updating the mental state of an agent in this new setting and three ways for comparing the resulting fuzzy set of desires have been given. Finally, two fundamental postulates any rational goal election function should obey have been stated

Learner modelling : optimizing training, assessment and testing

The authors are experimenting an innovative procedure to profile learners using an e-learning platform to predict if they will successfully end their training (or education activities) and to help tutors organize their tasks from the very beginning. Predictive learner modelling is proposed as an instrument for planning individual-oriented tutoring strategies to increase not only the probability of completion but also the return on investments of the training activities. In fact, by modelling learners\u2019 profiles it is possible to know in advance who of them will successfully complete their courses, who will leave the training anyway and who needs more help to complete their courses, according to their profiles. Knowing where learners are more likely to succeed will also help optimizing the assessment and training phases

Linguistic summarization of time series data using genetic algorithms

In this paper, the use of an evolutionary approach when obtaining linguistic summaries from time series data is proposed. We assume the availability of a hierarchical partition of the time dimension in the time series. The use of natural language allows the human users to understand the resulting summaries in an easy way. The number of possible final summaries and the different ways of measuring their quality has taken us to adopt the use of a multi objective evolutionary algorithm. We compare the results of the new approach with our previous greedy algorithms

Prediction of protein interactions on HIV-1-human PPI data using a novel closure-based integrated approach

Discovering Protein-Protein Interactions (PPI) is a new interesting challenge in computational biology. Identifying interactions among proteins was shown to be useful for finding new drugs and preventing several kinds of diseases. The identification of interactions between HIV-1 proteins and Human proteins is a particular PPI problem whose study might lead to the discovery of drugs and important interactions responsible for AIDS. We present the FIST algorithm for extracting hierarchical bi-clusters and minimal covers of association rules in one process. This algorithm is based on the frequent closed itemsets framework to efficiently generate a hierarchy of conceptual clusters and non-redundant sets of association rules with supporting object lists. Experiments conducted on a HIV-1 and Human proteins interaction dataset show that the approach efficiently identifies interactions previously predicted in the literature and can be used to predict new interactions based on previous biological knowledge

Prediction of protein interactions on HIV-1-human PPI data using a novel closure-based integrated approach

Discovering Protein-Protein Interactions (PPI) is a new interesting challenge in computational biology. Identifying interactions among proteins was shown to be useful for finding new drugs and preventing several kinds of diseases. The identification of interactions between HIV-1 proteins and Human proteins is a particular PPI problem whose study might lead to the discovery of drugs and important interactions responsible for AIDS. We present the FIST algorithm for extracting hierarchical bi-clusters and minimal covers of association rules in one process. This algorithm is based on the frequent closed itemsets framework to efficiently generate a hierarchy of conceptual clusters and non-redundant sets of association rules with supporting object lists. Experiments conducted on a HIV-1 and Human proteins interaction dataset show that the approach efficiently identifies interactions previously predicted in the literature and can be used to predict new interactions based on previous biological knowledge

An evolutionary algorithm for evaluation of emission compliance options in view of the clean air act amendments

10.1109/59.574956IEEE Transactions on Power Systems121336-341ITPS