Abstract and Keywords
This chapter focuses on genetic programming (GP), a stochastic optimization and model induction technique. An advantage of GP is that the modeler need not select the exact parameters to be used in the model beforehand. Rather, GP can effectively search a complex model space defined by a set of building blocks specified by the modeler. This flexibility has allowed GP to be used for many applications. The chapter reviews some of the most significant developments using GP: forecasting, stock selection, derivative pricing and trading, bankruptcy and credit risk assessment, and agent-based and economic modeling. Conclusions reached by studies investigating similar problems do not always agree; however, GP has proved useful across a wide range of problem areas. Recent and future work is increasingly concerned with adapting genetic programming to more dynamic environments and ensuring that solutions generalize robustly to out-of-sample data, to further improve model performance.
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