Vectorial Genetic Programming -- Optimizing Segments for Feature Extraction

TL;DR

This paper introduces Vectorial Genetic Programming (Vec-GP), which extends traditional GP by optimizing vector segments for feature extraction, and analyzes different strategies for segment aggregation optimization.

Contribution

It formalizes the optimization problem for segment aggregation in Vec-GP and evaluates various random and guided sampling strategies within this framework.

Findings

Random sampling strategies have minimal impact on performance.

Guided strategies tend to get stuck in local optima.

There is potential for developing more effective algorithms.

Abstract

Vectorial Genetic Programming (Vec-GP) extends GP by allowing vectors as input features along regular, scalar features, using them by applying arithmetic operations component-wise or aggregating vectors into scalars by some aggregation function. Vec-GP also allows aggregating vectors only over a limited segment of the vector instead of the whole vector, which offers great potential but also introduces new parameters that GP has to optimize. This paper formalizes an optimization problem to analyze different strategies for optimizing a window for aggregation functions. Different strategies are presented, included random and guided sampling, where the latter leverages information from an approximated gradient. Those strategies can be applied as a simple optimization algorithm, which itself ca be applied inside a specialized mutation operator within GP. The presented results indicate, that the different random sampling strategies do not impact the overall algorithm performance significantly, and that the guided strategies suffer from becoming stuck in local optima. However, results also indicate, that there is still potential in discovering more efficient algorithms that could outperform the presented strategies.