This paper presents results and observations from initial experiments with an aim to implement Evolutionary Computation to generate realistic fractal objects. The target fractals are both regular and irregular, commonly observed in nature.

Unexpected Patterns Generated while Attempting the Design of an Algorithm for Generating Natural Fractal Objects Using Evolutionary Computation

Down-sampling training data has long been shown to improve the generalization performance of a wide range of machine learning systems. Recently, down-sampling has proved effective in genetic programming (GP) runs that utilize the lexicase parent selection technique. Although this down-sampling procedure has been shown to significantly improve performance across a variety of problems, it does not seem to do so due to encouraging adaptability through environmental change. We investigate modifications to down-sampled lexicase selection in hopes of promoting incremental environmental change to scaffold evolution by reducing the amount of jarring discontinuities between the environments of successive generations. In our empirical studies, we find that promoting environmental change by rolling the down-sample across the entire training set is not significantly better for evolving solutions to program synthesis problems than simple random down-sampling. We also find that going in the other direction by using only disjoint down-samples also does not significantly differ from the performance of regular random down-sampling. These results highlight some new insights into the role of environmental change for down-sampled lexicase selection, and present a viable new direction for future research.

ALIFE 2022 Workshop WiDWS Submissions

Unexpected Patterns Generated while Attempting the Design of an Algorithm for Generating Natural Fractal Objects Using Evolutionary Computation

Exploring Environmental Change for Down-Sampled Lexicase Selection