Enhancement of genetic algorithm for diabetic patient diet planning

Abstract

Genetic Algorithm (GA) is an artificial intelligence (AI) based methodology for solving optimization problems. GA are problem dependent especially GA parameters and optimal parameter values require long experiment time. This project proposes a progress-value concept (PRGA) for crossover and mutation rate implement in steady-state GA (SSGA) to avoid trial and error experiment perform for optimal crossover and mutation rate. PRGA concept is using fitness value and total number of genes performed crossover and mutation for each individual within a generation to determine next generation crossover and mutation rate. PRGA is compare throughout SSGA with different fix crossover and mutation probability. The developed system is compiled using open source GA library (GAlib) for C programming language. Experimental results with proposed concept performance shows better processing time with SSGA.