Evolving asset selection using genetic network programming

As global financial innovation opens innumerable risks and opportunities, a global view of the asset allocation brings advantages in risk diversification for investments. We propose a novel framework for asset selection under global diversification principles using genetic network programming. Simulations using the stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show that the proposed framework is effective and offers competitive advantages against the conventional methods in finance and computational fields. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An evolutionary multi‐issue negotiation model

Intelligent agents are a promising development in improving the effectiveness of automated negotiations in the present complex business environments. Much research has been done that deal with a single issue, usually price, in a bilateral negotiation environment. On the other hand, in some studies on multiple issues, agents usually propose a combined offer on all the issues, or settle the negotiation issue by issue. We find that the combined offer on multiple issues is like a single‐issue negotiation. Issue‐by‐issue settlement can be considered as a series of single‐issue negotiations. Therefore a multi‐issue negotiation model is proposed in this paper in which multiple issues are a part of the negotiation process, not like a single‐issue settlement or combined offer on multiple issues. In the simulations, three issues, i.e. price, delivery time, and warranty, are considered, although it is possible to include more issues through the proposed system. In the simulations, a newly developed evolutionary computing technique called genetic network programming (GNP) is used and the characteristics and behaviors of agents are analyzed. To prove the effectiveness of the proposed system, the results of the proposed system are compared with those of conventional negotiation systems. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Stable disruption of ethanol production by deletion of the genes encoding alcohol dehydrogenase isozymes in Saccharomyces cerevisiae

We analyzed the effects of the deletions of genes encoding alcohol dehydrogenase (ADH) isozymes of Saccharomyces cerevisiae. The decrease in ethanol production by ADH1 deletion alone could be partially compensated by the upregulation of other isozyme genes, while the deletion of all known ADH isozyme genes stably disrupted ethanol production.     

Sub-10-fs deep-ultraviolet light source with stable power and spectrum

In some applications of ultrafast spectroscopy that employ sub-10-fs pulses, the pulse spectrum and power need to be stable for several tens of minutes. In this study, we generate sub-10-fs deep-ultraviolet (DUV) pulses with such stabilities by chirped-pulse four-wave mixing. A power fluctuation of less than 3% rms was realized by employing stabilization schemes that employ a power stabilizer. The pulses generated in this study have been applied to transient absorption spectroscopy in the DUV with a sub-10-fs time resolution [Phys. Chem. Chem. Phys.14, 6200 (2012).10.1039/c2cp23649d]. This sub-10-fs DUV source has a similar performance to widely used noncollinear optical parametric amplifiers.     

Stock price prediction using neural networks with RasID-GA

In general, neural networks are widely used in pattern recognition, system modeling and prediction, and can model complex nonlinear systems. In the previous work, we proposed a novel training algorithm, Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA), for training the multibranch recurrent neural networks recently developed. In this paper, RasID-GA has been applied to predict stock market prices using the multibranch feed forward neural networks. We predicted the next day's closing stock price with several past closing stock prices. We used the stock prices of 20 brands for 720 days in order to evaluate the generalization ability of the proposed method. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Chemical Properties of Fomblin® Perfluoropolyether Lubricants with Arylalkyl Groups on Hard-Disk Media

The chemical characteristics of perfluoropolyether lubricant films, which have arylalkyl groups at both ends of the main chain, were studied on hard-disk media. It was found by time-of-flight secondary ion mass spectroscopy (TOF-SIMS) that 94% of the functional groups of AM3001, (3,4-dioxomethylenephenyl)methyl, were eliminated from the main chain on a disk surface after a 672 h exposure, even in a regular clean room of 23°C and 55% RH. Regarding the lubricants synthesized by the authors, LUB-A having a benzyl functional group lost 35% of its functional group after a 768 h exposure. The 3-phenylpropyl functional group of LUB-B was not eliminated even after a 672 h exposure. It was suggested that hydrolysis easily occurred at the ether linkage between the main chain and the functional group, and removed the functional group from the main chain because the benzyl cation was chemically stable. The new lubricant having a 3-phenylpropyl functional group, which is chemically stable and does not decompose on a disk surface, was developed and proposed for use with hard-disk media.     

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.     

Nondestructive high-sensitivity measurement method for activation estimation in accelerator room concrete

This study established a method for easily and quickly estimating the specific activity produced in the concrete walls and floors of accelerator rooms during long-term operation of accelerator,for advanced zoning of activated/non-activated areas in planning the decommissioning of an accelerator.We propose a new,highly sensitive method for nondestructively estimating the specific activity in concrete that can be applied to activation zoning.In this method,instead of direct determination of the specific activities of important long-half-life radionuclides for decommissioning,such as ¹⁵²Eu and ⁶⁰Co,we determine the specific activities of short-half-life radionuclides,²⁴Na and ⁵⁶Mn,in situ to obtain neutron flux.The obtained neutron flux and accelerator operation history yield the specific activities of ¹⁵²Eu and ⁶⁰Co for the advance zoning of activated/non-activated concrete.This method is a powerful long-term prediction tool for concrete activation.     

Experimental Study of Condensation Heat Transfer on Tube Bundles in Large Power Plant Condensers

In order to clarify the effect of condensate inundation on steam condensation in a large tube bundle, condensation heat transfer and the condensate flow pattern in the tube bundle have been experimentally investigated. Test tube bundle consists of 36 cooling tubes, 12 condensate supply tubes and 24 un-cooled dummy tubes. Cooling test tubes are made of copper and have an outer diameter of 19.1 mm and condensing length of 150 mm, Steam flows horizontally through the test tube bundle at gap velocities 15-27 m/s at pressures of 8.8 kPa. In this study, experimental data about condensate flow pattern and condensation heat transfer in a tube bundle were collected for the optimization of tube arrangement in large power plant condensers.