# Understanding fitness landscapes in morpho-evolution via local optima networks

arXiv: https://arxiv.org/abs/2402.07822 · HTML: https://mlsanity.com/paper/2402.07822/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Local Optima Networks: A New Model of Combinatorial Fitness Landscapes](https://mlsanity.com/paper/1402.2959/) (2014)
- [Phenotype Search Trajectory Networks for Linear Genetic Programming](https://mlsanity.com/paper/2211.08516/) (2022)

## Built on by (most cited first, 4 of 4)

- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Controller Distillation Reduces Fragile Brain-Body Co-Adaptation and Enables Migrations in MAP-Elites](https://mlsanity.com/paper/2504.06523/) (2025)
- [When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?](https://mlsanity.com/paper/2505.22696/) (2025)
- [Evaluating Useful Surrogate Models for Configuration Tuning Beyond Accuracy: A Fitness Landscape Analysis Perspective](https://mlsanity.com/paper/2509.21945/) (2025)

## Abstract (arXiv)

Morpho-evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Previous research has provided empirical comparisons between encodings in terms of their performance with respect to an objective function and the diversity of designs that are evaluated, however there has been no attempt to explain the observed findings. We address this by applying Local Optima Network (LON) analysis to investigate the structure of the fitness landscapes induced by three different encodings when evolving a robot for a locomotion task, shedding new light on the ease by which different fitness landscapes can be traversed by a search process. This is the first time LON analysis has been applied in the field of ME despite its popularity in combinatorial optimisation domains; the findings will facilitate design of new algorithms or operators that are customised to ME landscapes in the future.