The advantages of genetic algorithm and neural networks to forecast air pollution trend in Malaysia: an overview / Mohamad Idham Md Razak … [et al.]

• Main Authors: Md Razak, Mohamad Idham, Ahmad, Ismail, Bujang, Imbarine, Talib, Adi Hakim, Kedin, Nor Adila
• Format: Book Section
• Language: English
• Published: Faculty of Administrative Science and Policy Studies 2012
• Subjects: Environmental conditions. Environmental quality. Environmental indicators. Environmental degradation; Electronic Computers. Computer Science; Algorithms
• Online Access: https://ir.uitm.edu.my/id/eprint/55451/1/55451.pdf; https://ir.uitm.edu.my/id/eprint/55451/

Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem. On the other hand, Neural Networks are composed of interconnecting artificial neurons. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. Thus, these two methods is the advance model for forecasting technique especially involving air pollution prediction and will be discuss as an overview of methodology in this particular research aspect.