Genetic Network Programming

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named ‘genetic network programming (GNP)’ that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ2 test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real-life database suggest that the proposed method provides an effective technique for handling continuous attributes. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named ‘genetic network programming (GNP)’ that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ2 test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real-life database suggest that the proposed method provides an effective technique for handling continuous attributes. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical database...

This research involves implementation of genetic network programming (GNP) and knapsack problem (KP)
to solve record clustering on distributed databases. The objective is to distribute big data to certain sites with the limited amount of capacities by considering the similarity of distributed data in each site. GNP is used to extract rules from big data by considering characteristics (value ranges) of each attribute in a dataset. KP is used to distribute rules to each site by considering similarity (value) and data amount (weight) related to each rule to match the site capacities.

The Elevator Group Supervisory Control Systems (EGSCS) are the control systems that systematically manage three or more elevators in order to efficiently transport the passengers in buildings. Double-deck elevators, where two cages are connected with each other, are expected to be the next generation elevator systems. Meanwhile, Destination Floor Guidance Systems (DFGS) are also expected in Double-Deck Elevator Systems (DDES). With these, the passengers could be served at two consecutive floors and could input their destinations at elevator halls instead of conventional systems without DFGS. Such systems become more complex than the traditional systems and require new control methods Genetic Network Programming (GNP), a graph-based evolutionary method, has been applied to EGSCS and its advantages are shown in some previous papers. GNP can obtain the strategy of a new hall call assignment to the optimal elevator because it performs crossover and mutation operations to

Genetic network programming (GNP) has been proposed as a new method of evolutionary computation. Until now, GNP has been applied to various problems and its effectiveness was clarified. However, these problems were virtual models, so the applicability and availability of GNP to the real-world applications have not been studied. In this paper, as a first step of applying GNP to the real-world applications, elevator group supervisory control systems (EGSCSs) are considered. Generally, EGSCSs are complex ...

A new graph-based evolutionary algorithm called “Genetic Network Programming, GNP" has been proposed. The solutions of GNP are represented as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement ...

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the elevator group system controller in some studies of recent years. Moreover, reinforcement learning (RL) is also verified to be useful for more improvements of elevator group performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group system of a typical office building to check its efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the elevator group system controller in some studies of recent years. Moreover, reinforcement learning (RL) is also verified to be useful for more improvements of elevator group performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group system of a typical office building to check its efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named 'genetic network programming (GNP)' that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ 2 test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real-life database suggest that the proposed method provides an effective technique for handling continuous attributes. 

In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the ...

Elevator group control systems are the control systems that systematically manage elevators in order to transport passengers efficiently. With the increasing need for high-performance transportation systems in buildings, multi-car elevators where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. Genetic network programming (GNP) can introduce various priori knowledge of the elevator systems in its node functions easily and execute an efficient ...

In this paper, we apply a Genetic Network Programming (GNP) Architecture using Automatically Defined Groups (ADG) to a multi-agent problem where cooperation of agents are required. GNP is a kind of evolutionary methods 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. In GNP with ADG, each agent is assigned to a group according to its role to complete some task of a cooperative problem. We consider two types of problems in this paper: one problem is to assign an appropriate role to each agent according to its ability, and the other is to assign a proper role to each agent with the same ability. While the first problem has the specific conditions as for the ability of an agent, the latter is a general problem. We show the effectiveness of GNP with ADG through computer simulations on the two types of load transportation problems.