How to assess multi-population genetic algorithms performance using intuitionistic fuzzy logic

Abstract

In this investigation a step-wise “cross-evaluation” procedure has been implemented aiming to assess the quality of multi-population genetic algorithms (MpGA) performance. Three MpGA, searching for an optimal solution applying main genetic operators selection, crossover and mutation in different order, have been here applied in such a challenging object as parameter identification of a fermentation process model. The performance quality of standard MpGA algorithm, denoted as MpGA_SCM (coming from selection, crossover, mutation), and two modifications, respectively MpGA_MCS (mutation, crossover, selection) and MpGA_CMS (crossover, mutation, selection) have been investigated for the purposes of parameter identification of S. cerevisiae fed-batch cultivation. As an alternative to conventional criteria for assessing the quality of algorithms performance, here an intuitionistic fuzzy logic (IFL) is going to be implemented. Also, this is the first time when two modifications of standard MpGA_SCM, in which the selection operator is performed as the last one, after crossover and mutation, are going to be evaluated. The performance of three MpGA is going to be assessed applying a step-wise procedure implementing IFL. As a result, MpGA_SCM has been approved as a leader between three considered here MpGA. The leadership between MpGA_CMS and MpGA_MCS depends on the researcher choice between a bit slower, but more highly evaluated MpGA_CMS towards faster one, but a bit less highly evaluated MpGA_MCS.