InterCriteria Analysis of Crossover and Mutation Rates Relations in Simple Genetic Algorithm

Abstract

Abstract-In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters -crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.

Some notes on intuitionistic fuzzy equivalence relations and their use on real data

“…The essence of the method is in the exhaustive pairwise comparison of the values of the measurements of all objects in the set against pairs of criteria, with all possible pairs being traversed, while counters being maintained for the percentage of the cases when the relations between the pairs of evaluations have been 'greater than', 'less than' or 'equal'. In these days ICA is used not just for comparison of criteria, but also for optimization of parameters (see [1,8,15,17]) and it was applied in number of areas of life (see [7,10,14,20,22]).…”

InterCriteria Analyzis of Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem

“…The approach ICrA has been applied for a large area of problems, e.g. [1], [2], [14], [25]. Published results show the applicability of the ICrA and the correctness of the approach.…”

InterCriteria Analysis of oncological data of the patients for the city of Burgas

“…The ICA approach has been applied for analyzing data and decision making in different areasmedical investigations [17,18,37,38,40,41], genetic algorithms [1,2,3,21,23,26,29], metaheuristic algorithms [10,11,12,13,14,15,16,19,22,27,28,30,31], neural networks [32,33,34,35], etc.…”