Evolutionary Computation is well-known for producing the solutions in optimization problems based on change, composition and selection. We have proposed Genetic Network Programming (GNP) [1, 2] as an extended method of Genetic Algorithm (GA) [3, 4] and Genetic Programming (GP) [5, 6]. It has been clarified that GNP is an effective method mainly for dynamic problems since GNP represents its solu...

This paper describes a hybrid stock trading system based on Genetic Network Programming (GNP) and Mean Conditional Value-at-Risk Model (GNP–CVaR). The proposed method, combining the advantages of evolutionary algorithms and statistical model, has provided useful tools to construct portfolios and generate effective stock trading strategies for investors with different risk-attitudes. Simulation ...

This paper presents a novel generic weight-based network bandwidth sharing policy and an available bit rate (ABR) algorithm that achieves this policy. Our policy supports the minimum cell rate (MCR) requirement and peak cell rate (PCR) constraint of each connection and allocates network bandwidth among all connections based on a weight associated with each connection. To achieve this policy for...

This paper presents a novel generic weight-based network bandwidth sharing policy and an available bit rate (ABR) algorithm that achieves this policy. Our policy supports the minimum cell rate (MCR) requirement and peak cell rate (PCR) constraint of each connection and allocates network bandwidth among all connections based on a weight associated with each connection. To achieve this policy for...

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 Program...

A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improve the expression ability and performance. Since GA, GP and GNP already proposed are based on evolution and they cannot change their solutions until one generation ends, we propose GNP with Reinforcement Learning (GNP with...

Abstract. In this study, we focus on Genetic Network Programming (GNP) which is the graph-based evolutionary algorithm. Similar to Genetic Algorithm (GA) and Genetic Programming (GP), GNP applies genetic operators to an individual, which is represented by a directed graph, in order to solve a given problem. GNP is usually applied to automatic generation of programs which control a mobile robot....

We explore the benefits of virtual concatenation in SONET/SDH-based traffic-groomable mesh networks. Our results qualitatively and quantitively demonstrate that virtual concatenation can improve bandwidth efficiency, simplify network control, and balance network load. 1. Background Optical SONET/SDH networks are the dominant infrastructure to support data and voice traffic in backbone and metro...

Even though bidirectional, high-quality and low-latency audio systems for network performance are available, the complexity involved in setting up remote sessions needs better tools andmethods to asses and tune network parameters. We present an implementation of a system to intuitively evaluate the Quality of Service (QoS) on best effort networks. In our implementation, musicians are able to co...