Created by W.Langdon from gp-bibliography.bib Revision:1.4685

- @Article{journals/asc/HaeriEF17,
- author = "Maryam Amir Haeri and Mohammad Mehdi Ebadzadeh and Gianluigi Folino",
- title = "Statistical genetic programming for symbolic regression",
- journal = "Applied Soft Computing",
- year = "2017",
- volume = "60",
- pages = "447--469",
- month = nov,
- keywords = "genetic algorithms, genetic programming",
- bibdate = "2017-11-22",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/asc/asc60.html#HaeriEF17",
- DOI = "doi:10.1016/j.asoc.2017.06.050",
- abstract = "n this paper, a new genetic programming (GP) algorithm for symbolic regression problems is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical informationâ€”such as variance, mean and correlation coefficientâ€”to improve GP. To this end, we define well-structured trees as a tree with the following property: nodes which are closer to the root have a higher correlation with the target. It is shown experimentally that on average, the trees with structures closer to well-structured trees are smaller than other trees. SGP biases the search process to find solutions whose structures are closer to a well-structured tree. For this purpose, it extends the terminal set by some small well-structured subtrees, and starts the search process in a search space that is limited to semi-well-structured trees (i.e., trees with at least one well-structured subtree). Moreover, SGP incorporates new genetic operators, i.e., correlation-based mutation and correlation-based crossover, which use the correlation between outputs of each subtree and the targets, to improve the functionality. Furthermore, we suggest a variance-based editing operator which reduces the size of the trees. SGP uses the new operators to explore the search space in a way that it obtains more accurate and smaller solutions in less time.",
- }

Genetic Programming entries for Maryam Amir Haeri Mohammad Mehdi Ebadzadeh Gianluigi Folino