%0 Journal Article %1 howard:1995:GA-P %A Howard, Les M. %A D'Angelo, Donna J. %D 1995 %J IEEE Expert %K algorithms, genetic programming %N 3 %P 11--15 %R doi:10.1109/64.393137 %T The GA--P: A Genetic Algorithm and Genetic Programming hybrid %V 10 %X The GA-P performs symbolic regression by combining the traditional genetic algorithm's function optimization strength with the genetic-programming paradigm to evolve complex mathematical expressions capable of handling numeric and symbolic data. This technique should provide new insights into poorly understood data relationships. Discovering relationships has been a task troubling researchers since the dawn of modern science. Discovering relationships between sets of data is laborious and error prone, and it is highly subject to researcher bias. Because many of today's research problems are more complex than those of the past, it is increasingly important that robust data analysis methods be available to researchers. For a data analysis method to be most useful, it must meet at least three criteria: good predictive ability, insight into the inner workings of the system being analyzed, and unbiased results. Historically, researchers deduced relationships solely by examining the data--a difficult task if the relationship is complex, if many variables are involved, or if the data are noisy (as often occurs in real-world problems).

@article{howard:1995:GA-P, abstract = {The GA-P performs symbolic regression by combining the traditional genetic algorithm's function optimization strength with the genetic-programming paradigm to evolve complex mathematical expressions capable of handling numeric and symbolic data. This technique should provide new insights into poorly understood data relationships. Discovering relationships has been a task troubling researchers since the dawn of modern science. Discovering relationships between sets of data is laborious and error prone, and it is highly subject to researcher bias. Because many of today's research problems are more complex than those of the past, it is increasingly important that robust data analysis methods be available to researchers. For a data analysis method to be most useful, it must meet at least three criteria: good predictive ability, insight into the inner workings of the system being analyzed, and unbiased results. Historically, researchers deduced relationships solely by examining the data--a difficult task if the relationship is complex, if many variables are involved, or if the data are noisy (as often occurs in real-world problems).}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Howard, Les M. and D'Angelo, Donna J.}, biburl = {https://www.bibsonomy.org/bibtex/2185dde12ae69137a10480293cf8382be/brazovayeye}, doi = {doi:10.1109/64.393137}, interhash = {7565a500f3655a5fa8080208f00cf6a1}, intrahash = {185dde12ae69137a10480293cf8382be}, journal = {IEEE Expert}, keywords = {algorithms, genetic programming}, month = {June}, notes = {University of Georgia. IEEE Expert Special Track on Evolutionary Programming (P. J. Angeline editor) \cite{angeline:1995:er}}, number = 3, pages = {11--15}, size = {5 pages}, timestamp = {2008-06-19T17:41:51.000+0200}, title = {The {GA--P}: {A} Genetic Algorithm and Genetic Programming hybrid}, volume = 10, year = 1995 }