Luque<ref>{{cite journal|last=Luque|first=Cristóbal|coauthorsauthor2=David Quintana, |author3=J. M. Valls, and |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=2384–2378|publisher=IEEE Press|___location=Piscatawy, NJ, USA}}</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==
Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there’s an alternative approach being researched to financial modeling called [[Agent-based model|agent-based modelling]] (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to complicated system dynamics that are sometimes impossible to predict from the properties of individual agents.<ref>{{cite journal |last=Brabazon |first=Anthony |coauthorsauthor2=Jiang Dang, |author3=Ian Dempsy, |author4=Michael O'Neill, and |author5=David M. Edelman |title=Natural Computing in finance: a review |journal=Handbook of Natural Computing |year=2010 |url=http://irserver.ucd.ie/dspace/bitstream/10197/2737/1/NCinFinance_v8.pdf |deadurl=yes}} {{dead link |date=September 2013}}</ref> ABM is starting to be applied to computational finance. Though, for ABMs to be more accurate, better models for rule-generation need to be developed.
