Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm

Title

Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm

Creator

Gunjan A.; Bhattacharyya S.; Hassanien A.E.

Description

Optimization of portfolios has an additional level of complexity and has been an area of interest for both financial leaders and artificial intelligence experts. In this article, a quantum-inspired version of an improved genetic algorithm is proposed for the task of portfolio optimization. An effort is made to implement two different genetic versions along with their extension in the quantum-inspired space. Improvements to the popular crossover techniques, viz. (i) arithmetic and (ii) heuristic crossover are proposed to reduce computational time. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Smart Innovation, Systems and Technologies, Vol-358, pp. 665-673.

Date

2023-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Genetic algorithms (ga); Particle swarm optimization (pso); Portfolio optimization; Quantum-inspired evolutionary algorithms

Coverage

Gunjan A., Christ (Deemed to be University), Bengaluru, India; Bhattacharyya S., Rajnagar Mahavidyalaya, Birbhum, India, Algebra University College, Zagreb, Croatia; Hassanien A.E., Faculty of Computers and Information, Cairo University, Giza, Egypt

Rights

Restricted Access

Relation

ISSN: 21903018; ISBN: 978-981993415-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-3416-4_53

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168774985&doi=10.1007%2f978-981-99-3416-4_53&partnerID=40&md5=3a1dce98bdbba874262170dba4471342