Co-Evolutionary Multi-Agent System for Portfolio Optimization

Dreżewski, Rafał; Siwik, Leszek

doi:10.1007/978-3-540-77477-8_15

Cited by 25 publications

(11 citation statements)

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“…The co-evolutionary multi-agent system using predator-prey interactions was presented in [69]. The system was run against well-known test problems as well as portfolio optimization problem.…”

Section: Previous Researchmentioning

confidence: 99%

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Dreżewski

Doroz

2017

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Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach-the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

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Section: Previous Researchmentioning

confidence: 99%

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Dreżewski

Doroz

2017

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“…Evolutionary multi-agent systems have been successfully applied to such problems as portfolio optimization [8], investment strategies generation [7] or machine learning [1].…”

Section: A Framework For a Evolutionary Multi-agent Systemmentioning

confidence: 99%

Niching in Evolutionary Multi-agent Systems

Krzywicki¹

2013

csci

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Niching is a group of techniques used in evolutionary algorithms, useful in several types of problems, including multimodal or nonstationary optimization. This paper investigates the applicability of these methods to evolutionary multi-agent systems (EMAS), a hybrid model combining the advantages of evolutionary algorithms and multi-agent systems. This could increase the efficiency of this type of algorithms and allow to apply them to a wider class of problems. As a starting point, a simple but flexible EMAS framework is proposed. Then, it is shown how to extend this framework in order to introduce niching, by adapting two classical niching methods. Finally, preliminary experimental results show the efficiency and the simultaneous discovery of multiple optima by this modified EMAS.

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“…Hochreiter [53] introduces an evolutionary stochastic portfolio optimisation methodology and illustrates its application using a set of structurally different risk measures, which include, Standard Deviation, Mean-absolute Downside Semi Deviation, Valueat-Risk, and Expected Shortfall. Recent work has also seen the application of co-evolutionary MOEAs for portfolio optimisation [38].…”

Section: Moea and Portfolio Selectionmentioning

confidence: 99%