Shinji Eto

Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization

Genetic network programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the elevator group supervisory control system (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such changes for practical applications. In this paper, the GNP with functional localization is applied to the EGSCS to construct such an adaptive system. In the proposed method, the switching GNP can switch the functionally localized GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic. From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building

Genetic Network Programing Considering the Evolution of Breadth and Depth

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named genetic network programming (GNP) has also been developed recently along with these trends. In this paper, a new method for evolving GNP considering breadth and depth is proposed. The performance of the proposed method is shown from simulations using garbage collector problem

Multi-car elevator group supervisory control system using Genetic Network Programming

Elevator group control systems are the transportation systems for handling passengers in the buildings. With the increasing demand for high-rise buildings, Multi-Car Elevator System(MCES) where two cars operate separately and independently in an elevator shaft are attracting attention as the next novel elevator system. Genetic Network Programming(GNP), one of the evolutionary computations, can realize a rule based MCES due to its directed graph structure of the individual, which makes the system more flexible. This paper discusses MCES using GNP for the buildings with 30 floors. The performance of MCES are examined and compared with Double-Deck Elevator System(DDES).

Genetic Network Programming with control nodes

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.

Multi-routes algorithm using temperature control of Boltzmann distribution in Q value-based dynamic programming

In this paper, we propose a heuristic method trying to improve the efficiency of traffic systems in the global perspective, where the optimal traveling time for each Origin-Destination (OD)pair is calculated by extended Q value-based Dynamic Programming and the global optimum routes are produced by adjusting the temperature parameter in Boltzmann distribution. The key point is that the temperature parameter for each section is not identical, but constantly changing with the traffic of the section, which enables the diversified routing strategy depending on the latest traffics. In addition, the simulation results show that comparing with the Greedy strategy and constant temperature parameter strategy, the proposed method, i.e., temperature parameter control strategy of the Q value-based Dynamic Programming with Boltzmann distribution, could reduce the traffic congestion effectively and minimize the negative impact of the information update interval by adopting suitable temperature parameter control strategy.

Functional localization of genetic network programming and its application to a pursuit problem

According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on genetic network programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.

Genetic Network Programming with Control Nodes considering Breadth and Depth

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named genetic network programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.

Switching for functional localization of genetic network programming

Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named genetic network programming (GNP) has also been developed recently along with these trends. The aim of this paper is to build an artificial model to realize functional localization based on GNP considering the fact that the functional localization of the brain is realized in such a way that a different part of the brain corresponds to a different function. GNP has a directed graph structure suitable for realizing functional localization. In this paper, it is especially stated that the evolution of the switching function can be realized for functional localization of GNP using the self-sufficient garbage collector problem.

Double-Deck Elevator Systems with idle cage assignment using Genetic Network Programming

Many studies on Double-Deck Elevator Systems (DDES) have been done for exploring more efficient algorithms to improve the system transportation capacity, especially in a heavy traffic mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. Unlike what occurs in this mode, where all cages almost always keep moving, there is the case, where some cages become idle in a light traffic mode. Therefore, how to dispatch these idle cages, which is seldom considered in the heavy traffic mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller with idle cage assignment algorithm using Genetic Network Programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the efficiency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern. Simulation results show that the proposed method improves the performance comparing to the case when the cage assignment algorithm is not employed and works better than six other heuristic methods in a light traffic mode.

A double-deck elevator systems controller with idle cage assignment algorithm using genetic network programming

Many studies on Double-Deck Elevator Systems (DDES) have been done for exploring more e±cient algorithms to improve the system transportation capacity, especially in a heavy tra±c mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. How to dispatch idle cages, which is seldom considered in the heavy tra±c mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller with idle cage assignment algorithm using Genetic Network Programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the effeciency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern.