advancing-gp-performance

#+title: Readme

• Advancing Genetic Programming Performance: Exploring a Penalized Pareto Replacement Approach and Enhanced Node Selection

Implementation of three novel strategies, GPDMD, GPCDMD, and ENS, detailed in the thesis document.

• GPDMD is a multi-objective replacement algorithm with a dynamic penalization selection scheme.
• GPCDMD extends this approach with stringent control over diversity through a probabilistic acceptance function.
• ENS is an efficient crossover algorithm that30 improves the likelihood of selecting a beneficial crossover point by using semantic information from training cases