Classical and Agent-Based Evolutionary Algorithms for Investment Strategies Generation

Dreżewski, Rafał; Sepielak, Jan; Siwik, Leszek

doi:10.1007/978-3-540-95974-8_9

Cited by 15 publications

(11 citation statements)

References 6 publications

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“…The agent-based co-evolutionary genetic programming approach was also applied with success to investment strategy generation [70,71]. During the experiments the agent-based approach was compared to the evolutionary algorithm, a co-evolutionary algorithm, and the buy-and-hold strategy.…”

Section: Previous Researchmentioning

confidence: 99%

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

Dreżewski

Doroz

2017

Symmetry

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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

Symmetry

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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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“…Evolutionary Multi-Agent System (EMAS) discussed for instance in (Dobrowolski & KisielDorohinicki, 2002;Cetnarowicz et al, 1996;Socha & Kisiel-Dorohinicki, 2002;Dreżewski & Siwik, 2008b;Dreżewski et al, 2009) proved to be very promising computational model. Unfortunately, at the same time, results obtained during solving multi-objective optimization problems (MOOPs) by distributed and decentralized agent-based evolutionary heuristic approach turned out to be not as high-quality as results obtained by classical equivalents and state-of-the-art algorithms.…”

Section: Shortcomings Of Naive Application Of Emas Model For Solving mentioning

confidence: 99%

Classical and Agent-Based Evolutionary Algorithms for Investment Strategies Generation

Cited by 15 publications

References 6 publications

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

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

Niching in Evolutionary Multi-agent Systems

Agent-Based Multi-Objective Evolutionary Algorithms with Cultural and Immunological Mechanisms

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