Behavioural GP diversity for dynamic environments

Yan, Wei; Clack, Christopher D.

doi:10.1145/1143997.1144290

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…Surprisingly, only a few scientific studies have focused their attention on promoting behavioural diversity in DOPs. Yan and Clack are among those few researchers promoting this type of diversity in a dynamic setting [17] by using the common semantic diversity method explained before.…”

Section: Previous Work On Promoting and Maintaining DIV Ersitymentioning

confidence: 99%

Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems

Galván

Elhara

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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In Evolutionary Algorithms (EAs), it is well-known that the adoption of diversity is highly beneficial for evolutionary search. This has also been explored and confirmed in Dyn3lnic Optimisation Problems (DOPs) using EAs. Multiple works have been proposed to encourage diversity in EAs in the face of a change, where the most common form to promote diversity is to replace a number of individuals by new genetic material. A common element when adopting this form of diversity is the fact that, frequently, the number of individuals to be replaced is picked rather arbitrarily. In this work, we propose the adoption of the Kendall tau distance that quantifies pairwise dissimilarities among two lists (of fitness values) with the hope to make a better informed decision in terms of the number of individuals that need to be replaced in a population by new individuals. Results on continuous fitness-valued cases indicate that the adopted distance is beneficial in DOPs.

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Section: Previous Work On Promoting and Maintaining DIV Ersitymentioning

confidence: 99%

Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems

Galván

Elhara

2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Later, based upon some rules it is decided if that particular asset should be included in the Portfolio. While some works have built rules related to moving averages of the assets [12], some others have used some Genetic Programming rules for the same [13], [14]. However, these Trading Strategies usually assign equal weights to all the assets after they have been selected for the Portfolio.…”

Section: Previous Workmentioning

confidence: 99%

Multi-objective portfolio optimization and rebalancing using genetic algorithms with local search

Soam

Palafox

Iba

2012

2012 IEEE Congress on Evolutionary Computation

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The Portfolio Optimization problem is an example of a resource allocation problem with money as the resource to be allocated to assets. We first have to select the assets from a pool of them available in the market and then assign proper weights to them to maximize the return and minimize the risk associated with the Portfolio. In our work, we have introduced a new "greedy coordinate ascent mutation operator" and we have also included the trading volumes concept. We performed simulations with the past data of NASDAQ100 and DowJones30, concentrating mainly on the 2008 recession period. We also compared our results with the indices and the simple Genetic Algorithms approach

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“…Wei and Clack use more complex GP rules, which include varied indicators of each assets financial performance to generate a decision to include or exclude an asset in a portfolio with a limited number of open "slots" [21,22].…”

Section: Asset Selectionmentioning

confidence: 99%

The Memetic Tree-based Genetic Algorithm and its application to Portfolio Optimization

Aranha

Iba

2009

Memetic Comp.

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We introduce a Memetic system to solve the application problem of Financial Portfolio Optimization. This problem consists of selecting a number of assets from a market and their relative weights to form an investment strategy. These weights must be optimized against a utility function that considers the expected return of each asset, and their co-variance; which means that as the number of available assets increases, the search space increases exponentially. Our method introduces two new concepts that set it apart from previous evolutionary based approaches. The first is the Tree-based Genetic Algorithm (GA), a recursive representation for individuals which allows the genome to learn information regarding relationships between the assets, and the evaluation of intermediate nodes. The second is the hybridization with local search, which allows the system to fine-tune the weights of assets after the tree structure has been decided. These two innovations make our system superior than other representations used for multi-weight assignment of portfolios.

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Behavioural GP diversity for dynamic environments

Cited by 15 publications

References 9 publications

Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems

Using fitness comparison disagreements as a metric for promoting diversity in Dynamic Optimisation Problems

Multi-objective portfolio optimization and rebalancing using genetic algorithms with local search

The Memetic Tree-based Genetic Algorithm and its application to Portfolio Optimization

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