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Open Access Journal Article

Evolutionary Computation Techniques for Portfolio Optimization

by Emily Taylor 1,*
1
Emily Taylor
*
Author to whom correspondence should be addressed.
TASC  2021 3(2):23; https://doi.org/10.69610/j.tasc.20211021
Received: 12 August 2021 / Accepted: 23 September 2021 / Published Online: 21 October 2021
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Abstract

The title "Evolutionary Computation Techniques for Portfolio Optimization" highlights the application of evolutionary computation methods to the complex problem of portfolio optimization. This abstract delves into the core of the research, which investigates the use of evolutionary algorithms, such as genetic algorithms, particle swarm optimization, and artificial bee colony algorithm, to enhance the efficiency and effectiveness of portfolio optimization strategies. The study focuses on the development of novel evolutionary computation techniques that can overcome the limitations of traditional optimization methods, such as local optima and computational complexity. By leveraging the robust search capabilities of evolutionary algorithms, the research aims to identify optimal portfolios that minimize risk while maximizing returns. The experimental results demonstrate that the proposed evolutionary computation-based strategies outperform conventional approaches in terms of both convergence speed and portfolio performance. This paper contributes to the field by providing a comprehensive analysis of evolutionary computation techniques and their practical implementation in portfolio optimization, offering new insights and potential solutions for financial institutions and investors.

Keywords: Evolutionary Computation, Portfolio Optimization, Genetic Algorithms, Particle Swarm Optimization, Artificial Bee Colony Algorithm.;
Copyright: © 2021 by Taylor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Taylor, E. Evolutionary Computation Techniques for Portfolio Optimization. Transactions on Applied Soft Computing, 2021, 3, 23. https://doi.org/10.69610/j.tasc.20211021
AMA Style
Taylor E. Evolutionary Computation Techniques for Portfolio Optimization. Transactions on Applied Soft Computing; 2021, 3(2):23. https://doi.org/10.69610/j.tasc.20211021
Chicago/Turabian Style
Taylor, Emily 2021. "Evolutionary Computation Techniques for Portfolio Optimization" Transactions on Applied Soft Computing 3, no.2:23. https://doi.org/10.69610/j.tasc.20211021
APA style
Taylor, E. (2021). Evolutionary Computation Techniques for Portfolio Optimization. Transactions on Applied Soft Computing, 3(2), 23. https://doi.org/10.69610/j.tasc.20211021

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