SIFT-SS: An Advanced Steady-State Multi-Objective Genetic Fuzzy System

2010 ◽  
pp. 1-8
Author(s):  
Michel González ◽  
Jorge Casillas ◽  
Carlos Morell
Keyword(s):  
Steady State ◽  
Fuzzy System ◽  
Multi Objective ◽  
Genetic Fuzzy System

scholarly journals Modeling and Optimizing the Multi-Objective Portfolio Optimization Problem with Trapezoidal Fuzzy Parameters

2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  
Keyword(s):  
Steady State ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Solution Process ◽  
Density Estimator ◽  
Nsga Ii ◽  
Spatial Spread ◽  
Multi Objective ◽  
Significant Difference ◽  
Portfolio Optimization Problem

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.

scholarly journals Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems

2015 ◽  
Vol 87 ◽  
pp. 47-60 ◽  
Author(s):  
Alexandru-Ciprian Zăvoianu ◽  
Edwin Lughofer ◽  
Werner Koppelstätter ◽  
Günther Weidenholzer ◽  
Wolfgang Amrhein ◽  
...  
Keyword(s):  
Steady State ◽  
Evolutionary Algorithms ◽  
Performance Comparison ◽  
Multi Objective ◽  
Computationally Intensive
Sign in / Sign up

Export Citation Format

Share Document