A Causal-Forecasting Model using Guided Genetic Algorithm in Continuous Manufacturing Process

연속생산공정에서의 유도형 유전알고리즘을 이용한 인과형 예측모델에 관한 연구

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.