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GENETIC ALGORITHMS: A HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS

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Abstract

Evolutionary algorithms are not new and have been developed, both concept and framework, around the 1950's on the idea that the evolution process could be used as 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. The views expressed in this document are the author's and do not necessarily reflect those of the organizations he is affiliated with. The content of this document is intended for information purpose only and is not an official statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHMS:
A HEURISTIC APPROACH
TO MULTI-DIMENSIONAL PROBLEMS
Written by Philippe Huber and Tony Guida
August 2019
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 dierent
solution every time it is run on the same data, it is remarkably
stable.
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
The views expressed in this document are the author’s and do not necessarily reect those of theorganizations he is aliated with. The content of this document is intended for
information purpose only and is not an ocial statement of RAM AI SA. No investment decision or particular course of action is recommended by this presentation from the author.
GENETIC ALGORITHM: AN HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS
GENETIC ALGORITHM:
AN HEURISTIC APPROACH
TO MULTI-DIMENSIONAL PROBLEMS
Written by Philippe Huber and Tony Guida
August 2019
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ResearchGate has not been able to resolve any references for this publication.