Kenichi Ida

A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem

The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio control. In this paper, we propose the modified genetic algorithm with search area adaptation (mGSA) for solving the Job-shop scheduling problem (JSP). Unlike GSA, our proposed method does not need such a crossover operator. To show the effectiveness of the proposed method, we conduct numerical experiments by using two benchmark problems. It is shown that this method has better performance than existing GAs.

Improved genetic algorithm for VLSI floorplan design with non-slicing structure

Floorplan design is an important engineering problem. This problem can be modeled as a combinatorial optimization problem, in which a given set of rectangles in floorplan must be arranged. The goal is to find the arrangement with minimum area and minimum interconnection. Floorplans can be classified into slicing structure and non-slicing structure problems. In both problem types, it is difficult to obtain a true optimal solution in a time that could be used in practice. We proposed the new immune algorithm for optimization of the slicing structure problem in the past. In this paper, we focus on and analyze the improved points considered to be especially effective, and propose a new genetic algorithm for the non-slicing structure problem. The proposed method is compared with existing methods using well-known benchmark problems.

Improved genetic algorithm for generalized transportation problem

In this paper, we introduce the genetic algorithm approach to the generalized transportation problem (GTP) and GTP with a fixed charge (fc-GTP). We focus on the use of Prüfer number encoding based on a spanning tree, which is adopted because it is capable of equally and uniquely representing all possible trees. From this point, we also design the criteria by which chromosomes can always be converted to a GTP tree. The genetic crossover and mutation operators are designed to correspond to the genetic representations. With the spanning-tree-based genetic algorithm, less memory space will be used than in the matrix-based genetic algorithm for solving the problem; thereby computing time will also be saved. In order to improve the efficiency of the genetic algorithm, we use the reduced cost for the optimality of a solution and the genetic algorithm to avoid degeneration of the evolutionary process. A comparison of results of numerical experiments between the matrix-based genetic algorithm and the spanning-tree-based genetic algorithm for solving GTP and fc-GTP problems is given.

Floorplan design problem using improved genetic algorithm

Genetic algorithms (GA) are applicable to many kinds of difficult problems. When a population keeps enough diversity and similarity, GA can obtain good solutions quickly. However, because these often compete with each other, it is difficult to fulfill both of these conditions simultaneously. In this article, taking these into consideration, we propose a new GA for the floorplan design problem, and aimed at improving the efficiency of calculation, the maintenance of the solution’s population diversity, and reduction of the number of parameters. We applied it to two MCNC (originally established as the Microelectronics Center of North Carolina) benchmark problems. The experimental results showed that the proposed method performed better than the existing methods.

Gaussian Mixture Distribution Analysis as Estimation of Probability Density Function and it's the Periphery

Abstract In statistics, Mixture distribution model is a stochastic model for a measured data set to express existence of the subpopulation in a population, without requiring that the subpopulation to whom each observational data belongs should be identified. Formally, Mixture distribution model is equivalent to expressing the probability distributions of observational data in a population. However, it is although it is related to the problem relevant to Mixture distribution pulling out a populations characteristic out of subpopulation. Mixture distribution model is used without subpopulations identity information in order to make the statistical inference about the characteristic of the subpopulation who was able to give only the observational data about a population simultaneously. This paper considered these matters from the similarity of the linear combination of an element function with estimation problem of a density function which used the Kernel function, and estimation problem of the density function using a Spline function. How to take Translate in arrangement of knots of estimation problem of the density function using the method of Bandwidth picking in estimation problem of the density function using a Kernel function and a Spline function and Wavelets analysis and Scale has a related thing.

Scheduling Problem for Allocating Worker with Class-Type Skill in JSP by Hybrid Genetic Algorithm

Scheduling in manufacturing systems is one of the most important and complex combinatorial optimization problems, where it can have a major impact on the productivity of a production process. Moreover, most of manufacturing scheduling models fall into the class of NP-hard combinatorial problems. In a real world manufacturing system, a plurality of worker who operates the machine exists, depending on the skill level by the workers for each machine and working time is different even if same work on the same machine in job-shop scheduling problem (JSP). Therefore, it is taking to account for differences in working time by the worker is scheduling problem with worker allocation. In this paper, in order to approach the more realistic model by dividing into several class workers and to determine the skill level for each machine for each class worker, we propose a new model that introduced the concept of class-type skill and demonstrate the effectiveness of the computational result by Hybrid Genetic Algorithm.

A Proposal of a Genetic Algorithm for Bicriteria Fixed Charge Transportation Problem

Transportation problem is a typical combinatorial problem. We aim at the search capacity of the solution by using a genetic algorithm with a the Bicriteria fixed Charge Transportation problem, which is an extension of the traditional transportation problem. In this paper, we improve the technique of Teramatu. In particular we propose new crossover operation for the genetic algorithm. Comparison with other methods is performed and the validity of the proposed method is shown.

GA for JSP with Delivery Time

This paper describes a job-shop scheduling problem (JSP) of processing product subject to no delay time job. It is one objective model of the minimum delivery delay time.In this paper, the effectiveness we the numerical experiments using a benchmark problem to improve the solution accuracy and decrease execution time by adding a method to generate gene and new approach, we introduce a search method for the algorithm shorter delivery times and further there to verify.

Advances in Multiobjective Hybrid Genetic Algorithms for Intelligent Manufacturing and Logistics Systems

Recently, genetic algorithms (GA) have received considerable attention regarding their potential as a combinatorial optimization for complex problems and have been successfully applied in the area of various engineering. We will survey recent advances in hybrid genetic algorithms (HGA) with local search and tuning parameters and multiobjective HGA (MO-HGA) with fitness assignments. Applications of HGA and MO-HGA will introduced for flexible job-shop scheduling problem (FJSP), reentrant flow-shop scheduling (RFS) model, and reverse logistics design model in the manufacturing and logistics systems.

The GMM Problem as One of the Estimation Methods of a Probability Density Function

In data analysis, we must be conscious of the probability density function of population distribution. Then it is a problem why the probability density function is expressed. n nThe estimation of a probability density function based on a sample of independent identically distributed observations is essential in a wide range of applications. The estimation method of probability density function x97 (1)a parametric method (2)a nonparametric method and (3)a semi-parametric method etc. x97 it is. In this paper, GMM problem is taken up as a semi-parametric method and We use a wavelet method as a powerful new technique. Compactly supported wavelets are particularly interesting because of their natural ability to represent data with intrinsically local properties.