Acceleration of cellular automata algorithms using genetic algorithms |
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| Affiliation: | 1. Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea;2. Department of Health Science and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea;3. Applied Electromagnetic Wave Research Center, Korea Electrotechnology Research Ins., 111, Hanggaul-ro, Ansan, Gyeonggi-do, Republic of Korea;4. Department of Energy and Power Conversion Engineering, University of Science & Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea;5. Chungwoo Co., Ltd., 2, Gasan digital 1-ro, Geumcheon-gu, Seoul, Republic of Korea;1. Servicio de Neumología, Complejo Hospitalario de Navarra, Pamplona, Navarra, España;2. Departamento de Salud, Universidad Pública de Navarra, Pamplona, Navarra, España;1. Department of Surgery, Onomichi General Hospital, Onomichi, Hiroshima, Japan;2. Department of Gastroenterological and Transplant Surgery, Applied Life Sciences, Institute of Biomedical and Health Sciences, Hiroshima University, Japan;3. Department of Gastroenterology,Onomichi General Hospital, Onomichi, Hiroshima, Japan;4. Kawasaki Medical School Hospital, Kurashiki, Okayama, Japan |
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| Abstract: | The following problem is solved: Given a Cellular Automaton with continuous state space which simulates a physical system or process, use a Genetic Algorithm in order to find a Cellular Automaton with discrete state space, having the smallest possible lattice size and the smallest possible number of discrete states, the results of which are as close as possible to the results of the Cellular Automaton with continuous state space. The Cellular Automaton with discrete state space evolves much faster than the Cellular Automaton with continuous state space. The state spaces of two Cellular Automata have been discretized using a Genetic Algorithm. The first Cellular Automaton simulates the two-dimensional photoresist etching process in integrated circuit fabrication and the second is used to predict forest fire spreading. A general method for the discretization of the state space of Cellular Automata using a Genetic Algorithm is also presented. The aim of this work is to provide a method for accelerating the execution of algorithms based on Cellular Automata (Cellular Automata algorithms) and to build a bridge between Cellular Automata as models for physical systems and processes and Cellular Automata as a VLSI architecture. |
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