병렬 유전자 프로그래밍을 이용한 Symbolic Regression
Symbolic regression based on parallel Genetic Programming
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
기호적 회귀분석 (Symbolic Regression)은 회귀분석에서 주어진 데이터에 대하여 종속변수와 독립변수들 사이의 관계를 설명할 수 있는 함수를 직접 생성하는 분석방법으로서 Genetic Programming 이 본 분야의 연구에 가장 선도적으로 적용되고 있으며, 고정된 모델로부터 매개변수들의 최적화를 추구하는 다른 회귀분석 알고리즘들에 비하여 해석이 가능한 모델을 직접 도출할 수 있다는 장점을 갖는다. 본 연구에서는 Coarse grained 병렬 모델에 기반한 Parellel Genetic Programming 을 이용한 symbolic regression 알고리즘을 제시하고 제시된 알고리즘을 PMLB 데이타에 적용하여 해당 알고리즘의 효용성을 분석하고자 한다.
Abstract
Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.
참고문헌 (15)
- Holland, J. H, "Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Atrificial Intelligence" (1975), MIT Press
- J. R. Koza, "Genetic Programming: On the Programming of Computers by Means of Natural Selection" (1992), MIT Press
- R. Poli, "A Field Guide to Genetic programming" (2008), Lulu Press, Inc
- Hossam Faris, "A Symbolic Regression Approach for Modeling the Temperature of Metal Cutting Tool", International Journal of Control and Automation, 6(4) (2013)
- D. A. Augusto, "Symbolic Regression via Genetic Programming", VI Brazilian Symposium on Neural Network, pp.173~178 (2000)
- B. McKay, "Using a Tree Structured Genetic Algorithm to Perform Symbolic Regression", First International Conference on Genetic Algorithms in Engineering Systems, 414, pp.1195~1202 (1995)
- Spears, W. M., "On the Virtues of Parameterized Uniform Crossover", Proceedings of the 4th International Conference on Genetic Algorithms, pp.230~236 (1991)
- Poli, R., "On the Search Properties of Different Crossover Operators in Genetic Programming", Proceedings of the Third Annual Conference, pp.293~301 (1998), University of Wisconsin
- U. M. O'Reilly, "An Analysis of Genetic Programming", Doctor, pp.1995
- "PMBL: A Large Benchmark Suite for Machine Learning Evaluation and Comparison, BioData Mining" (2017)[URL]
- W. Banzhaf, "Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications" (1998), Morgan Kaufmann Publishers Inc
- Silva, S., "Dynamic limists for bloat control in genetic programming and a review of past and current bloat theories", Genetic Programming and Evolvable Machines, 10 (2009)
- Angeline, P. J, "Advances in Genetic Programming", pp.75~98 (1994), MIT Press
- Luke, S, "Modification point depth and genome growth in genetic programming", Evolutionary Computation, 11, pp.67~106 (2003)
- Luke, S, "Two fast tree-creation algorithms for genetic programming", IEEE Transactions on Evolutionary Computation, 4, pp.274~283 (2000)