16,306 research outputs found
A divide and conquer method for symbolic regression
Symbolic regression aims to find a function that best explains the
relationship between independent variables and the objective value based on a
given set of sample data. Genetic programming (GP) is usually considered as an
appropriate method for the problem since it can optimize functional structure
and coefficients simultaneously. However, the convergence speed of GP might be
too slow for large scale problems that involve a large number of variables.
Fortunately, in many applications, the target function is separable or
partially separable. This feature motivated us to develop a new method, divide
and conquer (D&C), for symbolic regression, in which the target function is
divided into a number of sub-functions and the sub-functions are then
determined by any of a GP algorithm. The separability is probed by a new
proposed technique, Bi-Correlation test (BiCT). D&C powered GP has been tested
on some real-world applications, and the study shows that D&C can help GP to
get the target function much more rapidly
The Cascading Haar Wavelet algorithm for computing the Walsh-Hadamard Transform
We propose a novel algorithm for computing the Walsh-Hadamard Transform (WHT)
which consists entirely of Haar wavelet transforms. We prove that the
algorithm, which we call the Cascading Haar Wavelet (CHW) algorithm, shares
precisely the same serial complexity as the popular divide-and-conquer
algorithm for the WHT. We also propose a natural way of parallelizing the
algorithm which has a number of attractive features
Solving Set Constraint Satisfaction Problems using ROBDDs
In this paper we present a new approach to modeling finite set domain
constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We
show that it is possible to construct an efficient set domain propagator which
compactly represents many set domains and set constraints using ROBDDs. We
demonstrate that the ROBDD-based approach provides unprecedented flexibility in
modeling constraint satisfaction problems, leading to performance improvements.
We also show that the ROBDD-based modeling approach can be extended to the
modeling of integer and multiset constraint problems in a straightforward
manner. Since domain propagation is not always practical, we also show how to
incorporate less strict consistency notions into the ROBDD framework, such as
set bounds, cardinality bounds and lexicographic bounds consistency. Finally,
we present experimental results that demonstrate the ROBDD-based solver
performs better than various more conventional constraint solvers on several
standard set constraint problems
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