Multi-Attribute Portfolio Selection with Genetic Optimization Algorithms

2009 ◽  
Vol 47 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Lean Yu ◽  
Shouyang Wang ◽  
Kin Keung Lai
Keyword(s):  
Portfolio Selection ◽  
Optimization Algorithms ◽  
Genetic Optimization

Multi-Objective Optimization Formulation to Make Creative Teams in Teamology

2010 ◽  
Author(s):  
Masao Arakawa
Keyword(s):  
Optimization Algorithms ◽  
Project Based Learning ◽  
Project Teams ◽  
Genetic Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Learning Teams ◽  
Optimization Formulation

Teamology is established by Prof. Wilde to make creative teams in project teams. As a first step, it needs questionnaires to characterize personality for each member who joins the projects. Assume in academic project based learning teams, a number of students join and we are going to make several teams. Each team should have the same potential if and only if we can make every team as that. In order to create these teams, we need to quantify students’ characters, and we need to formalize them to meet the guideline of Teamology. In this study, we are going to make multi-objective optimization formulation of Teamology, and show an example of team making by using a genetic optimization algorithms with data that was taken PBL course in Kagawa University.

Genetic optimization algorithms in the design of coupled SAW filters

2005 ◽  
Author(s):  
J. Meltaus ◽  
P. Hamalainen ◽  
M.M. Salomaa ◽  
V.P. Plessky
Keyword(s):  
Optimization Algorithms ◽  
Genetic Optimization

scholarly journals Application of genetic optimization algorithms in decision-making systems

2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Andranik Akopov ◽  
Gayane Beklaryan
Keyword(s):  
Decision Making ◽  
Optimization Algorithms ◽  
Genetic Optimization

Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection

2016 ◽  
Vol 39 ◽  
pp. 48-63 ◽  
Author(s):  
Rubén Saborido ◽  
Ana B. Ruiz ◽  
José D. Bermúdez ◽  
Enriqueta Vercher ◽  
Mariano Luque
Keyword(s):  
Portfolio Selection ◽  
Optimization Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fuzzy Portfolio Selection

scholarly journals Distributed Computing for Genetic Approximation Algorithms for the Kissing Problem (n-dimensions)

2021 ◽  
Author(s):  
Andrew Kamal
Keyword(s):  
Distributed Computing ◽  
Optimization Algorithms ◽  
Genetic Optimization ◽  
Mathematical Proofs ◽  
Kissing Numbers ◽  
Higher Dimensional ◽  
Degree Of Certainty ◽  
Dimensional Complexity ◽  
Shared Network ◽  
Computational Processes

Utilizing distributed computing for genetic optimization algorithms with n-dimensions and kissing numbers in their approximation, helps offload data and increase efficiency in approximation. In order to demonstrate this, we shall utilize mathematical proofs centered around N-dimensional vectors and arrays, as well as exponential dimensional analysis. Utilizing these proofs in optimization algorithms can have processed data offloaded through a shared network of computers running simultaneous multi-threaded computational processes. One can build a computational model based off of mathematical constraints viewed as higher dimensional complexity. Formulating such proofs is based off of degree of certainty versus uncertainty in the approximation, and which processing task should be optimized in order to yield the best result.

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