Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Structure-specified IIR filter and control design using real structured genetic algorithm
Applied Soft Computing
A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps
Applied Soft Computing
Enhanced index tracking based on multi-objective immune algorithm
Expert Systems with Applications: An International Journal
Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem
Expert Systems with Applications: An International Journal
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Robust optimization framework for cardinality constrained portfolio problem
Applied Soft Computing
A hybrid stock selection model using genetic algorithms and support vector regression
Applied Soft Computing
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
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This paper proposes a heuristic searching approach on construction of a tracking portfolio, which is able to get the average market return and can even outperform some hedge funds that are managed actively. The tracking portfolio is expected to replicate the performance of a benchmark index return with a part of its component stocks while reducing the cost of transaction by limiting the number of rebalancing and unnecessary investment on less influential component stocks. The mathematical model being proposed is based on a hybrid genetic algorithm with a self-adaptive evolving mechanism. In order to enhance the model efficiency, we optimize the original genetic algorithm by applying Pareto efficiency as utility measure and goal programming for the inevitable conflicts of multiple objectives/interests. The proposed approach provides a comprehensive solution to index tracking problem by considering as many practical issues as possible. The constructed portfolio has a satisfactory performance on experiments based on CSI300, FTSE100 and HSI data. The proposed formulation of index tracking is therefore believed to be a good alternative to many current techniques.