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GENETIC ALGORITHMS: A HEURISTIC APPROACH TO MULTI-DIMENSIONAL PROBLEMS

August 2019

Project: Evolutionary algorithms (EA) in Finance

Authors:

RAM Active Investment

Université Savoie Mont Blanc

Preprints and early-stage research may not have been peer reviewed yet.

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.

Content uploaded by Tony Guida

Content may be subject to copyright.

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

ResearchGate has not been able to resolve any citations for this publication.

ResearchGate has not been able to resolve any references for this publication.

Project

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Introduce applications of EA in finance by illustrating added value, challenges and opportunities given from thoses methods.

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Article

Optimization of the difference patterns for monopulse antennas by a hybrid real/integer-coded differ...

February 2005 · IEEE Transactions on Antennas and Propagation

The optimization of difference patterns of monopulse antennas is considered. The synthesis problem is recast as an optimization problem by defining a suitable cost function. In particular, in this paper, the cost function is based on constraints on the side-lobe levels. A subarray configuration is adopted and the excitations of the difference pattern are approximately determined. The optimization ... [Show full abstract] problem is efficiently solved by a differential evolution algorithm, which is able to contemporarily handle real and integer unknowns. Numerical results are reported concerning classical array configurations previously considered in the literature.

Conference Paper

Rule extraction from neural networks trained using evolutionary algorithms with deterministic mutati...

June 1998

A method of extracting rules from neural networks trained using evolutionary algorithms (EAs) is presented. The EAs used are a genetic algorithm (GA) with deterministic mutation (DM) and a random optimization method (ROM) with DM. The DM is performed on the basis of the result of neural network learning. It can evolve chromosomes of individuals to increase their fitness functions in a ... [Show full abstract] deterministic manner. The EAs are utilized to reduce the number of neural network connections. The network connections surviving after training represent rules to perform pattern classification. The rules are then extracted from the network in which hidden units use signum functions to produce binary outputs. Simulation results show this method can generate a simple network structure and as a result simple rules for the iris data classification

Conference Paper

The simplex method and evolutionary algorithm

June 1998

S. Smith

Nelder and Mead's (1965) simplex algorithm has often been used in many evolutionary algorithms as a `local hill-climber' to try and improve the rate of convergence. Typically, every so often, a simplex is formed about a single (good) individual and then the simplex algorithm is run separately from the generational system. In this paper, we show that by defining each vertex of the simplex to be an ... [Show full abstract] individual selected from the population, and `evolving' it for a small number of function evaluations, a stable genetic operator is produced, which can used in conjunction with other genetic operators. We also prove that this genetic operator can be coded in such away that the operator will not fail, computationally, regardless of the choice of vertices

Article

Coello, C.A.C.: An updated survey of GA-based multi-objective optimization techniques. ACM Computing...

February 1970 · ACM Computing Surveys

Carlos A. Coello Coello

After using evolutionary techniques for single-objective optimization during more than two decades, the incorporation of more than one objective in the fitness function has finally become a popular area of research. As a consequence, many new evolutionary-based approaches and variations of existing techniques have recently been published in the technical literature. The purpose of this paper is ... [Show full abstract] to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the operations research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new ways of exploiting the search capabilities of evolutionary algorithms. Furthermore, a summary of the main algorithms behind these approaches is provided, together with a brief criticism that includes their advantages and disadvantages, degree of applicability, and some known applications. Finally, further trends in this area and some possible paths for further research are also addressed.

Last Updated: 05 Jul 2022