Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving robust GP solutions for hedge fund stock selection in emerging markets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Building Risk-Optimal Portfolio Using Evolutionary Strategies
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Regularization approach to inductive genetic programming
IEEE Transactions on Evolutionary Computation
Application of a Memetic Algorithm to the Portfolio Optimization Problem
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using GAs to balance technical indicators on stock picking for financial portfolio composition
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Steepest ascent hill climbing for portfolio selection
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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Recently, a number of works have been done on how to use Genetic Algorithms to solve the Portfolio Optimization problem, which is an instance of the Resource Allocation problem class. Almost all these works use a similar genomic representation of the portfolio: An array, either real, where each element represents the weight of an asset in the portfolio, or binary, where each element represents the presence or absence of an asset in the portfolio. In this work, we explore a novel representation for this problem. We use a tree structure to represent a portfolio for the Genetic Algorithm. Intermediate nodes represent the weights, and the leaves represent the assets. We argue that while the Array representation has no internal structure, the Tree approach allows for the preservation of building blocks, and accelerates the evolution of a good solution. The initial experimental results support our opinions regarding this new genome representation. We believe that this approach can be used for other instances of Resource Allocation problems.