Hybrid Symbolic Regression with the Bison Seeker Algorithm
نویسندگان
چکیده
منابع مشابه
Hybrid Seeker Optimization Algorithm for Global Optimization
Swarm intelligence algorithms have been succesfully applied to hard optimization problems. Seeker optimization algorithm is one of the latest members of that class of metaheuristics and it has not yet been thorougly researched. Since the early versions of this algorithm were less succesful with multimodal functions, we propose in this paper hybridization of the seeker optimization algorithm wit...
متن کاملElite Bases Regression: A Real-time Algorithm for Symbolic Regression
Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In thi...
متن کاملA Hybrid GP Approach for Numerically Robust Symbolic Regression
This article introduces a hybrid variant of genetic programming (GP) for doing symbolic regression. Instead of the usual interpretation of a parse tree, all top-level terms are identified and extended by multiplying them with locally optimized factors. These weighted terms are then linearly combined to form the resulting expression. When using the mean square error as fitness function, local op...
متن کاملHybrid Regression-Classification Models for Algorithm Selection
Many state of the art Algorithm Selection systems use Machine Learning to either predict the run time or a similar performance measure of each of a set of algorithms and choose the algorithm with the best predicted performance or predict the best algorithm directly. We present a technique based on the well-established Machine Learning technique of stacking that combines the two approaches into ...
متن کاملSymbolic Regression Algorithms with Built-in Linear Regression
Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude) faster than SR algorithms based on vanilla ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: MENDEL
سال: 2019
ISSN: 2571-3701,1803-3814
DOI: 10.13164/mendel.2019.1.079