Stepwise Adaptation of Weights for Symboli

Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming

In this paper we continue study on the Stepwise Adaptation of Weights (saw) technique. Previous studies on constraint satisfaction and data classification have indicated that saw is a promising technique to boost the performance of evolutionary algorithms. Here we use saw to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance o...

Symboli Representation of Upward-Closed Sets

Genetic Programming with Historically Assessed Hardness

We present a variation of the genetic programming algorithm, called Historically Assessed Hardness (HAH), in which the fitness rewards for particular test cases are scaled in proportion to their relative difficulty as gauged by historical solution rates. The method is similar in many respects to some realizations of techniques such as implicit fitness sharing, stepwise adaptation of weights and...

Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generati...

Adaptive penalties for evolutionary

In this paper we consider a problem independent constraint handling mechanism, Stepwise Adaptation of Weights (SAW) and show its working on graph coloring problems. SAWing technically belongs to the penalty function based approaches and amounts to modifying the penalty function during the search. We show that it has a twofold ben-eet. First, it proves to be rather insensitive to its technical p...