著者

小野 功 小林 重信

出版者

社団法人人工知能学会

雑誌

人工知能学会誌 (ISSN:09128085)

巻号頁・発行日

vol.13, no.5, pp.780-790, 1998-09-01

被引用文献数

13

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In this paper, we propose a new genetic algorithm(GA) for job-shop scheduling problems(JSPs), considering dependencies among machines. We regard the crossover as a main search operator. Crossovers should preserve characteristics between parents and their children in order for GAs to perform well. Characteristics are elements that constitute a solution and determine the fitness of the solution. Chracteristics also should be highly independent of each other. A characteristic has to be found for each problem domain since it depends on a particular problem domain. We basically regard the processing order of jobs as a characteristic for JSPs. We consider job-based order inheritance and position-based order inheritance for ways of inheritance of the processing order by crossovers, and propose two new crossovers; the Inter-machine Job-based Order Crossover(Inter-machine JOX) and the Inter-machine Position-based Order Crossover(Inter-machine POX). By applying them to the benchmark problems of FT10×10 and FT20×5, we demonstrate that the Inter-machine JOX shows better performance than the Inter-machine POX and an existing crossover, the SXX[Kobayashi 95]. The Inter-machine JOX preserves both the processing order of jobs and the technological ordering which causes dependencies among machines. We also propose a new mutation named the Inter-machine Job-based Shift Change for introducing a diversity of population. We confirm its effectiveness by applying it with the Inter-machine JOX to FT10×10 and FT20×5.

著者

佐久間 淳 安藤 晋 小林 重信

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.23, no.3, pp.163-175, 2008 (Released:2008-02-26)

参考文献数

17

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In the process of mixture model estimation using Expectation-Maximization (EM) methods, mixture densities are required to be measured at every step to obtain posterior probabilities. When the number of data n in a dataset or the number of mixtures m is large, the time complexity required for the evaluation of posterior probabilities is O(mn).