Shouichi Matsui

Genetic algorithm can optimize hierarchical menus

Hierarchical menus are now ubiquitous. The performance of the menu depends on many factors: structure, layout, colors and so on. There has been extensive research on novel menus, but there has been little work on improving the performance by optimizing the menus structure. This paper proposes an algorithm based on the genetic algorithm (GA) for optimizing the performance of menus. The algorithm aims to minimize the average selection time of menu items by considering movement and decision time. We show results on a static hierarchical menu of a cellular phone where a small screen and limited input device are assumed. Our work makes several contributions: a novel mathematical optimization model for hierarchical menus; novel optimization method based on the genetic algorithm (GA).

Adaptive multiagent model of electric power market with congestion management

We propose a new multiagent simulation model for analyzing the characteristics of the electricity market. The model maintains important features of the actual market, and congestion management is also built-in. We can discuss adequate rules of the electric power market and investigate the behavior of market price by the proposed model.

An efficient hybrid genetic algorithm for a fixed channel assignment problem with limited bandwidth

We need an efficient channel assignment algorithm for increasing channel re-usability, reducing call-blocking rate and reducing interference in any cellular systems with limited bandwidth and a large number of subscribers. We propose an efficient hybrid genetic algorithm for a fixed channel assignment problem with limited bandwidth constraint. The proposed GA finds a good sequence of codes for a virtual machine that produces channel assignment. Results are given which show that our GA produces far better solutions to several practical problems than existing GAs.

An empirical performance evaluation of a parameter-free genetic algorithm for job-shop scheduling problem

The Job-Shop Scheduling Problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Several GA-based approaches have been reported for the JSSP. Among them, there is a parameter-free genetic algorithm (PfGA) for JSSP proposed by Matsui et al., based on an extended version of PfGA, which uses random keys for representing permutation of operations in jobs, and uses a hybrid scheduling for decoding a permutation into a schedule. They reported that their algorithm performs well for typical benchmark problems, but the experiments were limited to a small number of problem instances. This paper shows the results of an empirical performance evaluation of the GA for a wider range of problem instances. The results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.

Real-Coded Parameter-Free Genetic Algorithm for Job-Shop Scheduling Problems

We propose a new genetic algorithm (GA) for job-shop scheduling problems (JSSP) based on the parameter-free GA (PfGA) and parallel distributed PfGA proposed by Sawai et al. The PfGA is not only simple and robust, but also does not need to set almost any genetic parameters in advance that need to be set in other GAs. The performance of PfGA is high for functional optimization problems of 5- or 10-dimensions, but its performance for combinatorial optimization problems, which search space is larger than the functional optimization, has not been investigated. We propose a new algorithm for JSSP based on an extended PfGA, extended to real-coded version. The GA uses random keys for representing permutation of jobs. Simulation results show that the proposed GA can attain high quality solutions for typical benchmark problems without parameter tuning.

A Parameter-Free Genetic Algorithm for a Fixed Channel Assignment Problem with Limited Bandwidth

Increasing the channel re-usability is necessary for reducing the call-blocking rate in any cellular systems with limited bandwidth and a large number of subscribers. To increase the re-usability, we need an efficient channel assignment algorithm that minimizes the sum of blocking cost and interference cost. We propose a new genetic algorithm for the problem based on the parameter-free GA. The proposed GA finds a good sequence of codes for a virtual machine that produces channel assignment. Results are given which show that our GA, without tedious parameter tuning, produces far better solutions to several practical problems than the existing GAs.

A genetic algorithm for optimizing hierarchical menus

Hierarchical menus are widely used as a standard user interface in modern applications that use GUIs. The performance of the menu depends on many factors: structure, layout, colors and so on. There has been extensive research on novel menus, but there has been little work on improving performance by optimizing the menupsilas structure. This paper proposes algorithms based on the genetic algorithm (GA) and the simulated annealing (SA) for optimizing the performance of menus. The algorithms aim to minimize the average selection time of menu items by considering the userpsilas pointer movement and search/decision time. We will show the results on a static hierarchical menu of a cellular phone as an example where a small screen and limited input device are assumed. We will also show performance comparison of GA-based algorithm and the SA-based one by using wide variety of the usage patterns.

Performance evaluation of a parameter-free genetic algorithm for job-shop scheduling problems

The job-shop scheduling problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1].

Performance evaluation of a genetic algorithm for optimizing hierarchical menus

Hierarchical menus are now widely used as standard user interfaces in modern applications with GUIs. The menu performance depends on many factors, such as the structure, layout, and colors. There has been extensive research on novel hierarchical menus, but there has been little work on improving performance by optimizing the menus structure. We have proposed an algorithm based on a genetic algorithm (GA) for optimizing the performance of menus. The algorithm aims to minimize the average selection time of menu items by taking into account movement and decision-making time. We have shown that the proposed algorithm can reduce average selection time nearly 40% for a menu of a cellar phone. But usage pattern were limited and the accuracy of the model was not confirmed. We will first show the validation result of the model by experiments conducted on PDA. Then we will present results of the performance evaluation of the algorithm by using a wide variety of usage patterns generated by Zipf function. The results show that the model has good accuracy for real users, and the algorithm can attain good results for a wide variety of usage patterns.

Empirical Performance Evaluation of a Parameter-Free GA for JSSP

The job-shop scheduling problem (JSSP) is a well known di.cult NP-hard problem. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error. To address this problem, Sawai et al. have proposed the Parameter-free GA (PfGA), for which no control parameters for genetic operation need to be set in advance [3].