Kaizen programming

title = "Kaizen programming",

booktitle = "GECCO '14: Proceedings of the 2014 conference on Genetic and evolutionary computation",

year = "2014",

editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",

isbn13 = "978-1-4503-2662-9",

pages = "895--902",

keywords = "genetic algorithms, genetic programming",

month = "12-16 " # jul,

organisation = "SIGEVO",

address = "Vancouver, BC, Canada",

URL = "

http://doi.acm.org/10.1145/2576768.2598264",

DOI = "

10.1145/2576768.2598264",

publisher = "ACM",

publisher_address = "New York, NY, USA",

abstract = "This paper presents Kaizen Programming, an evolutionary tool based on the concepts of Continuous Improvement from Kaizen Japanese methodology. One may see Kaizen Programming as a new paradigm since, as opposed to classical evolutionary algorithms where individuals are complete solutions, in Kaizen Programming each expert proposes an idea to solve part of the problem, thus a solution is composed of all ideas together. Consequently, evolution becomes a collaborative approach instead of an egocentric one. An idea's quality (analog to an individual's fitness) is not how good it fits the data, but a measurement of its contribution to the solution, which improves the knowledge about the problem. Differently from evolutionary algorithms that simply perform trial-and-error search, one can determine, exactly, parts of the solution that should be removed or improved. That property results in the reduction in bloat, number of function evaluations, and computing time. Even more important, the Kaizen Programming tool, proposed to solve symbolic regression problems, builds the solutions as linear regression models - not linear in the variables, but linear in the parameters, thus all properties and characteristics of such statistical tool are valid. Experiments on benchmark functions proposed in the literature show that Kaizen Programming easily outperforms Genetic Programming and other methods, providing high quality solutions for both training and testing sets while requiring a small number of function evaluations.",

notes = "Also known as \cite{2598264} GECCO-2014 A joint meeting of the twenty third international conference on genetic algorithms (ICGA-2014) and the nineteenth annual genetic programming conference (GP-2014)",