A causal-forecasting model using guided genetic algorithm in continuous manufacturing process

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guided 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 investigating 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.