How to Assess Multi-population Genetic Algorithms Performance Using Intuitionistic Fuzzy Logic

2018 ◽  
pp. 23-35 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Population Genetic ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

2015 ◽  
pp. 1125-1152
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Batch Cultivation ◽  
Generation Gap ◽  
Model Parameters ◽  
Fed Batch ◽  
Intuitionistic Fuzzy ◽  
Before And After ◽  
Intuitionistic Fuzzy Logic ◽  
Fed Batch Cultivation

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

scholarly journals How to Assess Different Algorithms Using Intuitionistic Fuzzy Logic

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2189
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Evdokia Sotirova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Process Model ◽  
Linear Optimization ◽  
Main Tool ◽  
Optimization Techniques ◽  
Evaluation Procedure ◽  
Genetic Operators ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic

Intuitionistic fuzzy logic is the main tool in the recently developed step-wise “cross-evaluation” procedure that aims at the assessment of different optimization algorithms. In this investigation, the procedure previously applied to compare the effectiveness of two or three algorithms has been significantly upgraded to evaluate the performance of a set of four algorithms. For the first time, the procedure applied here has been tested in the evaluation of the effectiveness of genetic algorithms (GAs), which are proven as very promising and successful optimization techniques for solving hard non-linear optimization tasks. As a case study exemplified with the parameter identification of a S. cerevisiae fed-batch fermentation process model, the cross-evaluation procedure has been executed to compare four different types of GAs, and more specifically, multi-population genetic algorithms (MGAs), which differ in the order of application of the three genetic operators: Selection, crossover and mutation. The results obtained from the implementation of the upgraded intuitionistic fuzzy logic-based procedure for MGA performance assessment have been analyzed, and the standard MGA has been outlined as the fastest and most reliable one among the four investigated algorithms.

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

2014 ◽  
pp. 327-354 ◽  
Author(s):  
Tania Pencheva ◽  
Maria Angelova ◽  
Krassimir Atanassov
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Batch Cultivation ◽  
Generation Gap ◽  
Model Parameters ◽  
Fed Batch ◽  
Intuitionistic Fuzzy ◽  
Before And After ◽  
Intuitionistic Fuzzy Logic ◽  
Fed Batch Cultivation

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

scholarly journals A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ali Muhammad Rushdi ◽  
Mohamed Zarouan ◽  
Taleb Mansour Alshehri ◽  
Muhammad Ali Rushdi
Keyword(s):  
Fuzzy Logic ◽  
Propositional Logic ◽  
Compact Form ◽  
Logical Function ◽  
Intuitionistic Fuzzy ◽  
Complete Product ◽  
Single Function ◽  
Intuitionistic Fuzzy Logic

The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premisesallthat can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.

REMARK ON THE PSEUDO-FIXED POINTS OF THE INTUITIONISTIC FUZZY LOGIC OPERATORS

2001 ◽  
Vol 09 (01) ◽  
pp. 77-80 ◽  
Author(s):  
KRASSIMIR T. ATANASSOV ◽  
NIKOLAI G. NIKOLOV
Keyword(s):  
Fuzzy Logic ◽  
Modal Logic ◽  
Fixed Points ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic

The question of finding the pseudo-fixed points of the operators defined in the frameworks of the intuitionistic fuzzy modal logic is investigated.

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