In this paper, we propose Genetic Network Programming (GNP) Architecture using Automatically Defined Groups. GNP is a kind of new evolutionary method inspired from Genetic Programming (GP). While GP has a tree architecture, GNP has a network architecture, with which an agent works in the virtual world. Because only one network architecture is evolved for agents in a system in previous works, ev...

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases becaus...

A new evolutionary computation method named Genetic Network Programming (GNP) was proposed recently. In this paper, an online learning method for GNP is proposed. This method uses Q learning to improve its state transition rules so that it can make GNP adapt to the dynamic environments efficiently.

In the real implementation of autonomous mobile robots, the environments change dynamically with unknown and inexperienced situations, whichmake agents do tasks inappropriately. In order to behave appropriately, the agents should be robust to unknown environments and also have the adaptability mechanisms to change the structures/parameters of their controllers adaptively when inexperienced situ...

In this paper, we propose a genetic network programming (GNP) architecture using a coevolution model called automatically defined groups (ADG). The GNP evolves networks for describing condition-action relations for agents. By applying ADG to GNP, we evolve different networks in order to realize the cooperation of multiple agents with different abilities. Computational experiments on a load tran...

The credit derivatives market has experienced remarkable growth over the past decade. As such, there is a growing interest in tools for pricing of the most prominent credit derivative, the credit default swap (CDS). In this paper, we propose a heuristic algorithm for pricing of basket default swaps (BDS). For this purpose, genetic network programming (GNP), which is one of the recent evolutiona...

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...

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....

Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. In this paper, GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties in this paper: 1) we use conditional Sharp ratio as a risk...