A model of portfolio optimization using time adapting genetic network programming

This paper describes a decision-making model of dynamic portfolio optimization for adapting to the change of stock prices based on an evolutionary computation method named genetic network programming (GNP). The proposed model, making use of the information from technical indices and candlestick chart, is trained to generate portfolio investment advice. Experimental results on the Japanese stock market show that the decision-making model using time adapting genetic network programming (TA-GNP) method outperforms other traditional models in terms of both accuracy and efficiency. A comprehensive analysis of the results is provided, and it is clarified that the TA-GNP method is effective on the portfolio optimization problem.     

Self‐Healing Micellar Ion Gels Based on Multiple Hydrogen Bonding

Ion gels, composed of macromolecular networks filled by ionic liquids (ILs), are promising candidate soft solid electrolytes for use in wearable/flexible electronic devices. In this context, the introduction of a self‐healing function would significantly improve the long‐term durability of ion gels subject to mechanical loading. Nevertheless, compared to hydrogels and organogels, the self‐healing of ion gels has barely investigated been because of there being insufficient understanding of the interactions between polymers and ILs. Herein, a new class of supramolecular micellar ion gel composed of a diblock copolymer and a hydrophobic IL, which exhibits self‐healing at room temperature, is presented. The diblock copolymer has an IL‐phobic block and a hydrogen‐bonding block with hydrogen‐bond‐accepting and donating units. By combining the IL and the diblock copolymer, micellar ion gels are prepared in which the IL phobic blocks form a jammed micelle core, whereas coronal chains interact with each other via multiple hydrogen bonds. These hydrogen bonds between the coronal chains in the IL endow the ion gel with a high level of mechanical strength as well as rapid self‐healing at room temperature without the need for any external stimuli such as light or elevated temperatures.     

A portfolio optimization model using Genetic Network Programming with control nodes

Many evolutionary computation methods applied to the financial field have been reported. A new evolutionary method named “Genetic Network Programming” (GNP) has been developed and applied to the stock market recently. The efficient trading rules created by GNP has been confirmed in our previous research. In this paper a multi-brands portfolio optimization model based on Genetic Network Programming with control nodes is presented. This method makes use of the information from technical indices and candlestick chart. The proposed optimization model, consisting of technical analysis rules, are trained to generate trading advice. The experimental results on the Japanese stock market show that the proposed optimization system using GNP with control nodes method outperforms other traditional models in terms of both accuracy and efficiency. We also compared the experimental results of the proposed model with the conventional GNP based methods, GA and Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than these methods.     

Study of multi-branch structure of Universal Learning Networks

In this paper, multi-branch structure of Universal Learning Networks (ULNs) is studied to verify its effectiveness for obtaining compact models, which have neurons connected with other neurons using more than two branches having nonlinear functions. Multi-branch structure has been proved to have higher representation/generalization ability and lower computational cost than conventional neural networks because of the nonlinear function of the multi-branches and the reduction of the number of neurons to be used. In addition, learning of delay elements of multi-branch ULNs has improved their potential to build up a compact dynamical model with higher performances and lower computational cost when applied for identifying dynamical systems.     

Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. Genetic Network Programming (GNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model.     

Polarisation control of a loop antenna by PIN diodes

A wire loop antenna with two electronic switches is introduced to control polarisation (left-hand, right-hand or linear polarisation). The loop is fed through an electromagnetically coupled monopole antenna. The loop and the monopole are located on a ground plane. The switching circuit with PIN diodes is installed near the feed point of the monopole to obtain symmetric radiation patterns. Good axial ratio and impedance characteristics are obtained. The experimental results show the capability of the polarisation control by electronic switches     

A robust control method for a PV-supplied DC motor using universal learning networks

In this paper, a new robust control method and its application to a photovoltaic (PV) supplied, separately excited DC motor loaded with a constant torque is discussed. The robust controller is designed against the load torque changes by using the first and second ordered derivatives of the universal learning networks (ULNs). These derivatives are calculated using the forward propagation algorithm, which is considered as an extended version of real time recurrent learning (RTRL). In this application, two ULNs are used: The first is the ULN identifier trained offline to emulate the dynamic performance of the DC motor system. The second is the ULN controller, which is trained online not only to make the motor speed follow a selected reference signal, but also to make the overall system operate at the maximum power point of the PV source. To investigate the effectiveness of the proposed robust control method, the simulation is carried out at four different values of the robustness coefficient γ in two different stages: The training stage, in which the simulation is done for a constant load torque. And the control stage, in which the controller performance is obtained when the load torque is changed. The simulation results showed that the robustness of the control system is improved although the motor load torque at the control stage is different from that at the training stage.     

One-day- through seven-day-ahead electrical load forecasting in consideration of uncertainties of weather information

This paper proposes an approach to one-day-through seven-day-ahead electrical load forecasting based on a realistic problem formulation which should contribute to more reliable and economic weekly power stations operation. Generally, the load forecasting has the following problems:

• 1 (1) although the load is affected by various factors, such as temperatures, in the load forecasting, it is impossible to consider all of them;
• 2 (2) the relationships between the load and some factors are not clear, and often vary with time; and
• 3 (3) uncertainties in forecasts of the temperatures sometimes make the results of load forecasting worse.

They are very influential in the power station operation. While a number of methods have been proposed to solve the problems (1) and (2), there have been few attempts to solve the problem (3). The following approach is proposed in this paper, taking these problems into consideration. First, concerning the problem (1), the focus is on such factors that have major influence on the load and whose values are obtainable on a weekly basis. The other factors are all regarded as stochastic and are not included in the forecasting model. Second, regarding the problem (2), a self-organizing approach is used where the algorithm itself finds the optimal model structure or the optimal set of factors to be included in the model day by day. Finally, addressing the problem (3), a new performance index of model structures is proposed which can measure the balance between: i) improvement of the load-forecasting accuracy due to inclusion of a factor in the model; and ii) degradation caused by uncertainty or error in the factor included. Using this index, a model is constructed which does not yield a large error in spite of errors in the temperature forecasts. Examples show that this approach improves the forecasted results when erroneous temperature forecasts are fed into the model, and verifies its effectiveness.     

An extended probabilistic model building genetic network programming using both of good and bad individuals

Classical estimation of distribution algorithms (EDAs) generally use truncation selection to estimate the distribution of the good individuals while ignoring the bad ones. However, various researches in evolutionary algorithms (EAs) have reported that the bad individuals may affect and help solving the problem. This paper proposes a new method to use the bad individuals by studying the substructures rather than the entire individual structures to solve reinforcement learning (RL) problems, which generally factorize their entire solutions to the sequences of state–action pairs. This work was studied in a recent graph‐based EDA named probabilistic model building genetic network programming (PMBGNP), which could solve RL problems successfully, to propose an extended PMBGNP. The effectiveness of this work is verified in an RL problem, namely robot control. Compared to other related work, results show that the proposed method can significantly speed up the evolution efficiency. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.