Different Length Genetic Algorithm-Based Clustering of Indian Stocks for Portfolio Optimization

2018 ◽  
pp. 45-59
Author(s):  
Somnath Mukhopadhyay ◽  
Tamal Datta Chaudhuri
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

Portfolio optimization using genetic algorithm and harmony search algorithm with varying operators and parameter values

2019 ◽  
Author(s):  
Kee Huong Lai ◽  
Woon Jeng Siow ◽  
Ahmad Aniq bin Mohd Nooramin Kaw ◽  
Pauline Ong ◽  
Zarita Zainuddin
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Parameter Values

The Genetic Algorithm

2019 ◽  
pp. 154-178
Author(s):  
Burcu Adıguzel Mercangöz ◽  
Ergun Eroglu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Models ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Biological Evolution ◽  
Research Field ◽  
Risk Level ◽  
Important Research ◽  
Portfolio Optimization Problem

The portfolio optimization is an important research field of the financial sciences. In portfolio optimization problems, it is aimed to create portfolios by giving the best return at a certain risk level from the asset pool or by selecting assets that give the lowest risk at a certain level of return. The diversity of the portfolio gives opportunity to increase the return by minimizing the risk. As a powerful alternative to the mathematical models, heuristics is used widely to solve the portfolio optimization problems. The genetic algorithm (GA) is a technique that is inspired by the biological evolution. While this book considers the heuristics methods for the portfolio optimization problems, this chapter will give the implementing steps of the GA clearly and apply this method to a portfolio optimization problem in a basic example.

scholarly journals An Effective Approach for the Diverse Group Stock Portfolio Optimization Using Grouping Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 155871-155884
Author(s):  
Chun-Hao Chen ◽  
Cheng-Yu Lu ◽  
Tzung-Pei Hong ◽  
Jerry Chun-Wei Lin ◽  
Matteo Gaeta
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Diverse Group ◽  
Grouping Genetic Algorithm ◽  
Stock Portfolio

scholarly journals Portfolio Optimization Based on Funds Standardization and Genetic Algorithm

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 21885-21900 ◽  
Author(s):  
Yao-Hsin Chou ◽  
Shu-Yu Kuo ◽  
Yi-Tzu Lo
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization

Portfolio Optimization by Fuzzy Interactive Genetic Algorithm

2018 ◽  
pp. 124-131
Author(s):  
Masato Sasaki ◽  
Anas Laamrani ◽  
Mitsuo Yamashiro ◽  
Chalew Alehegn ◽  
Ariel Kamoyedji
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Interactive Genetic Algorithm

Portfolio optimization problems in different risk measures using genetic algorithm

2009 ◽  
Vol 36 (7) ◽  
pp. 10529-10537 ◽  
Author(s):  
Tun-Jen Chang ◽  
Sang-Chin Yang ◽  
Kuang-Jung Chang
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Optimization Problems ◽  
Risk Measures
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