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===Rule-based system===
For example, Quintana<ref>{{cite journal|last=Quintana|first=David |author2=Cristóbal Luque |author3=Pedro Isasi|title=Evolutionary rule-based system for IPO underpricing prediction|journal=In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (GECCO '05)|year=2005|pages=983–989|doi=10.1145/1068009.1068176 |hdl=10016/4081 |isbn=1595930108 |s2cid=3035047 |hdl-access=free}}</ref> first abstracts a model with 7 major variables. The rules evolved from the Evolutionary Computation system developed at Michigan and Pittsburgh:
* Underwriter prestige – Is the underwriter prestigious in role of lead manager? 1 for true, 0 otherwise.
* Price range width – The width of the non-binding reference price range offered to potential customers during the roadshow. This width can be interpreted as a sign of uncertainty regarding the real value of the company and a therefore, as a factor that could influence the initial return.
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===Two-layered evolutionary forecasting===
Luque<ref>{{cite journal|last=Luque|first=Cristóbal|author2=David Quintana |author3=J. M. Valls |author4=Pedro Isasi |title=Two-layered evolutionary forecasting for IPO underpricing|journal=In Proceedings of the Eleventh Conference on Congress on Evolutionary Computation (CEC'09)|year=2009|pages=2374–2378|publisher=IEEE Press|___location=Piscatawy, NJ, USA|doi=10.1109/cec.2009.4983237|isbn=978-1-4244-2958-5|s2cid=1733801}}</ref> approaches the problem with outliers by performing linear regressions over the set of data points (input, output). The algorithm deals with the data by allocating regions for noisy data. The scheme has the advantage of isolating noisy patterns which reduces the effect outliers have on the rule-generation system. The algorithm can come back later to understand if the isolated data sets influence the general data. Finally, the worst results from the algorithm outperformed all other algorithms' predictive abilities.
==Agent-based modelling==
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