Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Reconstructing Algebraic Functions from Mixed Data
SIAM Journal on Computing
SAW-ing EAs: adapting the fitness function for solving constrained problems
New ideas in optimization
Six subprograms for curve fitting using splines under tension
Communications of the ACM
Bayesian Methods for Efficient Genetic Programming
Genetic Programming and Evolvable Machines
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Adapting the Fitness Function in GP for Data Mining
Proceedings of the Second European Workshop on Genetic Programming
Artificial Life
Test-case generator for nonlinear continuous parameter optimizationtechniques
IEEE Transactions on Evolutionary Computation
Genetic Programming: A Parallel Approach
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Evolving Fuzzy Decision Trees with Genetic Programming and Clustering
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
An autonomous GP-based system for regression and classification problems
Applied Soft Computing
On the importance of data balancing for symbolic regression
IEEE Transactions on Evolutionary Computation
Hoeffding bound based evolutionary algorithm for symbolic regression
Engineering Applications of Artificial Intelligence
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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 generating polynomials which we then use to further test all three GP variants.