# Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition

arXiv: https://arxiv.org/abs/2103.00480 · HTML: https://mlsanity.com/paper/2103.00480/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2021.

## Built on by (most cited first, 3 of 3)

- [On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne](https://mlsanity.com/paper/2112.09697/) (2021)
- [NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming](https://mlsanity.com/paper/2403.19459/) (2024)
- [NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks](https://mlsanity.com/paper/2404.08786/) (2024)

## Abstract (arXiv)

Semantic diversity in Genetic Programming has proved to be highly beneficial in evolutionary search. We have witnessed a surge in the number of scientific works in the area, starting first in discrete spaces and moving then to continuous spaces. The vast majority of these works, however, have focused their attention on single-objective genetic programming paradigms, with a few exceptions focusing on Evolutionary Multi-objective Optimization (EMO). The latter works have used well-known robust algorithms, including the Non-dominated Sorting Genetic Algorithm II and the Strength Pareto Evolutionary Algorithm, both heavily influenced by the notion of Pareto dominance. These inspiring works led us to make a step forward in EMO by considering Multi-objective Evolutionary Algorithms Based on Decomposition (MOEA/D). We show, for the first time, how we can promote semantic diversity in MOEA/D in Genetic Programming.