Genetic Algorithms: A Heuristic Approach to Multi-Dimensional Problems

20 Pages Posted: 13 Sep 2019

See all articles by Philippe Huber

Philippe Huber

RAM Active Investments

Tony Guida

Université de Savoie - Finance and Banking; RAM Active Investments

Date Written: August 30, 2019

Abstract

Evolutionary algorithms are not new and have been developed, both their concepts and framework, since around the 1950’s based on the idea that the evolutionary process could be used as a general-purpose optimization tool. The goal of this paper is to propose an alternative to classical optimization techniques that can handle systems of a very high dimension. With the rapid rise of computing power, as well as the augmentation of alternative sources of data, quantitative analysts are confronted by numerical challenges that didn’t exist a decade ago. In this paper, we show that a Genetic Algorithm (GAs) is a simple process based on the evolution paradigm that is well adapted to very large portfolios, increasing the execution speed; an optimization of a portfolio of more than 100’000 times series of 5’000 daily returns takes less than 5 minutes. Finally, we illustrate that, although GAs are a random process that generates a different solution every time it is run on the same data, it is remarkably stable.

Keywords: genetic algorithms, dimensionality reduction, portfolio optimisation

JEL Classification: G0, C6, C8

Suggested Citation

Huber, Philippe and Guida, Tony, Genetic Algorithms: A Heuristic Approach to Multi-Dimensional Problems (August 30, 2019). Available at SSRN: https://ssrn.com/abstract=3451302 or http://dx.doi.org/10.2139/ssrn.3451302

Philippe Huber (Contact Author)

RAM Active Investments ( email )

8 rue du rhone
geneva, 1204
Switzerland

Tony Guida

Université de Savoie - Finance and Banking ( email )

27 Rue Marcoz
Chambéry, 73011
France

RAM Active Investments ( email )

8 rue du rhone
geneva, 1204
Switzerland

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