Symbolic Regression In Design Of Experiments: A Case Study With Linearizing Transformations
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Symbolic and numerical regression: experiments and applications
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Evolution of robustness in digital organisms
Artificial Life
Resilient Individuals Improve Evolutionary Search
Artificial Life
The root causes of code growth in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Genetic programming for medical classification: a program simplification approach
Genetic Programming and Evolvable Machines
The impact of population size on code growth in GP: analysis and empirical validation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Elitism reduces bloat in genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
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Recently there has been considerable interest in determining whether, and how much, evolutionary pressure for genetic robustness influences evolutionary processes. In this paper, we attempt to show that this evolutionary pressure does have a significant effect in typical genetic programming problems. Specifically we demonstrate that in a standard genetic programming implementation to solve a symbolic regression problem, pressure for genetic robustness forces the population away from high fitness, but less robust, solutions in favor of solutions with lower fitness, but higher genetic robustness.