Intelligent Mutation Rate Control in an Economic Application of Genetic Algorithms

Maschek, Michael K.

doi:10.1007/s10614-009-9190-6

Cited by 13 publications

(14 citation statements)

References 12 publications

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“…GAs have been used to predict the financial failure of firms (Acosta-González and Fernández, 2014), to explain the 2008 financial crisis (Acosta-González et al, 2012), to model exchange rates (Lawrenz and Westerhoff, 2003), to evaluate the convergence to the rational expectations equilibrium (Maschek, 2010), to optimize the signals generated by technical trading tools (Thinyane and Millin, 2011), to forecast stock price trends in Taiwan (Wei, 2013), etc. See Drake and Marks (2002) for a review of the applications of GAs in financial forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

2017

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Section: Literature Reviewmentioning

confidence: 99%

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

2017

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“…To facilitate comparison, we consider exactly the same Cournot market as Alkemade et al (2006Alkemade et al ( , 2007Alkemade et al ( , 2009). For other studies in which quantity competition among firms is modeled using EAs, we refer to Arifovic (1994), Price (1997), Dawid and Kopel (1998), Franke (1998), Vriend (2000, Arifovic and Maschek (2006), Wheeler et al (2006), Casari (2008), and Maschek (2010.…”

Section: Cournot Oligopoly Marketmentioning

confidence: 99%

“…Usually, an ordinary binary encoding of strategies is used, like in EA1 and EA2. Examples of the use of Gray codings of strategies are provided by Arifovic (1996) and Maschek (2010). Arifovic states that "the Gray coding ensures that, if a small number of bits within a binary string change, this will correspond to a small change in a decoded integer or real number" (p. 525).…”

Section: Ea6mentioning

confidence: 99%

“…For early research in which genetic algorithms are employed, we refer to Miller (1986Miller ( , 1996, Axelrod (1987), Marks (1992), Arifovic (1994Arifovic ( , 1996, Andreoni and Miller (1995), and Dawid (1996). Examples of more recent research can be found in the work of, among others, Lux and Schornstein (2005), Alkemade et al (2006Alkemade et al ( , 2007Alkemade et al ( , 2009), Arifovic and Maschek (2006), Wheeler et al (2006), Xu (2006), Casari (2008), and Maschek (2010).…”

Section: Introductionmentioning

confidence: 99%

“…If the agents whose behavior is being modeled have to make decisions that are intrinsically binary, such as decisions between cooperation and defection in a prisoner's dilemma (e.g., Axelrod 1987), the use of a binary encoding of strategies is a very natural choice. However, in the case of nonbinary decisions, such as decisions by firms on their production level (e.g., Arifovic 1994;Price 1997;Dawid and Kopel 1998;Franke 1998;Vriend 2000;Alkemade et al 2006Alkemade et al , 2007Alkemade et al , 2009; Arifovic and Maschek 2006;Wheeler et al 2006;Casari 2008;Maschek 2010), there is no clear reason for the use of a binary encoding of strategies.…”

Section: Introductionmentioning

confidence: 99%

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Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies

2010

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We are concerned with evolutionary algorithms that are employed for economic modeling purposes. We focus in particular on evolutionary algorithms that use a binary encoding of strategies. These algorithms, commonly referred to as genetic algorithms, are popular in agent-based computational economics research. In many studies, however, there is no clear reason for the use of a binary encoding of strategies. We therefore examine to what extent the use of such an encoding may influence the results produced by an evolutionary algorithm. It turns out that the use of a binary encoding can have quite significant effects. Since these effects do not have a meaningful economic interpretation, they should be regarded as artifacts. Our findings indicate that in general the use of a binary encoding is undesirable. They also highlight the importance of employing evolutionary algorithms with a sensible economic interpretation.

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Particle Swarm Optimization in Agent‐Based Economic Simulations of the Cournot Market Model

Maschek

2015

Intell. Sys. Acc. Fin. Mgmt.

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Summary The numerous variations of the particle swarm optimization (PSO) algorithm originally proposed by Kennedy and Eberhart (. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks IV. IEEE: Piscataway, NJ; 1942–1948) have proven to be powerful optimization methods that rely on exploiting simple analogues of social interaction. In this study, PSO is adopted in lieu of the social or individual evolutionary learning algorithms as a model of individual adaptation in an agent‐based computational model. In this examination of the simple Cournot market framework, each agent's individual strategy evolves according to the PSO algorithm. The model is one in which agents’ strategies must adapt interdependently. That is, a change in one particle may not only affect its performance but also other particles within the same swarm simultaneously. The dynamics and convergence properties associated with this model are compared with those where evolutionary learning algorithms are employed. Similar to evolutionary learning, convergence to equilibrium is dependent on the scope of learning, social or individual. While convergence is dependent on some of the algorithm parameters, prices resulting from the individual PSO are nearest the Cournot equilibrium and those from social PSO are nearest the Walrasian equilibrium in all cases. For particular parameterizations, certain advantages over evolutionary algorithms exist: in the main, decreasing volatility in market prices does not require an election operator or the addition of free parameters through two‐level learning. Copyright © 2015 John Wiley & Sons, Ltd.

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Intelligent Mutation Rate Control in an Economic Application of Genetic Algorithms

Abstract: Evolutionary algorithm, Social learning, Mutation rate control, Cobweb model,

Cited by 13 publications

References 12 publications

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

Let the Data Do the Talking: Empirical Modelling of Survey-Based Expectations by Means of Genetic Programming

Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies

Particle Swarm Optimization in Agent‐Based Economic Simulations of the Cournot Market Model

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