Introduction to probability and statistics (7th ed.)
Introduction to probability and statistics (7th ed.)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
A multi-objective evolutionary approach to the portfolio optimization problem
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
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
Non-linear factor model for asset selection using multi objective genetic programming
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Overfitting or poor learning: a critique of current financial applications of GP
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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Multiple Objective Genetic Programming (MOGP) is a promising stock-picking technique for fund managers, because the Pareto front approximates the risk/reward Efficient Frontier and simplifies the choice of investment model for a given client's attitude to risk. Unfortunately GP solutions don't work well if used in an environment that is different from the training environment, and the financial markets are notoriously unstable, often lurching from one market context to another (e.g. "bull" to "bear"). This turns out to be a hard problem -- simple dynamic adaptation methods are insufficient and robust behaviour of solutions becomes extremely important. In this paper we provide the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data. We focus on two well-known mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation. We introduce novel metrics for Pareto front robustness, and a novel variation on Mating Restriction, both based on phenotypic cluster analysis.