Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Adaptive global optimization with local search
Adaptive global optimization with local search
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Behavioural GP diversity for dynamic environments: an application in hedge fund investment
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
A tree-based GA representation for the portfolio optimization problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A new framework for assets selection based on dimensions reduction techniques
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Modesty is the best policy: automatic discovery of viable forecasting goals in financial data
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Steepest ascent hill climbing for portfolio selection
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
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The Portfolio Optimization problem consists of the selection of a group of assets to a long-term fund in order to minimize the risk and maximize the return of the investment. This is a multi-objective (risk, return) resource allocation problem, where the aim is to correctly assign weights to the set of available assets, which determines the amount of capital to be invested in each asset. In this work, we introduce a Memetic Algorithm for portfolio optimization. Our system is based on a tree-structured genome representation which selects assets from the market and establish relationships between them, and a local hill climbing function which uses the information available from the tree-structure to calculate the weights of the selected assets. We use simulations based on historical data to test our system and compare it to previous approaches. In these experiments, our system shows that it is able to adapt to aggressive changes in the market, like the crash of 2008, with reduced trading cost.