{"title":"Machine Learning in Production Systems Design Using Genetic Algorithms","authors":"Abu Qudeiri Jaber, Yamamoto Hidehiko Rizauddin Ramli","volume":18,"journal":"International Journal of Industrial and Manufacturing Engineering","pagesStart":801,"pagesEnd":809,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10797","abstract":"To create a solution for a specific problem in machine\r\nlearning, the solution is constructed from the data or by use a search\r\nmethod. Genetic algorithms are a model of machine learning that can\r\nbe used to find nearest optimal solution. While the great advantage of\r\ngenetic algorithms is the fact that they find a solution through\r\nevolution, this is also the biggest disadvantage. Evolution is inductive,\r\nin nature life does not evolve towards a good solution but it evolves\r\naway from bad circumstances. This can cause a species to evolve into\r\nan evolutionary dead end. In order to reduce the effect of this\r\ndisadvantage we propose a new a learning tool (criteria) which can be\r\nincluded into the genetic algorithms generations to compare the\r\nprevious population and the current population and then decide\r\nwhether is effective to continue with the previous population or the\r\ncurrent population, the proposed learning tool is called as Keeping\r\nEfficient Population (KEP). We applied a GA based on KEP to the\r\nproduction line layout problem, as a result KEP keep the evaluation\r\ndirection increases and stops any deviation in the evaluation.","references":"[1] G. Tompkins and F. Azadivar, \"Genetic algorithms in optimizing\r\nSimulated Systems\", In WSC -95. Proceeding of the 1995 Conference on\r\nWinter Simulation, ACM, 1995, pp. 757-762.\r\n[2] S. Forrest, \"Genetic Algorithms\", ACM Computing Surveys, Vol. 28 No.\r\n1, 1996, pp. 77-83.\r\n[3] D. Lawrence, Handbook of Genetic Algorithms, Van No strand Reinhold,\r\nNew York, 1991.\r\n[4] D. 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