# Evolutionary computation for multicomponent problems: opportunities and future directions

arXiv: https://arxiv.org/abs/1606.06818 · HTML: https://mlsanity.com/paper/1606.06818/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2016.

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

- [Solving Travelling Thief Problems using Coordination Based Methods](https://mlsanity.com/paper/2310.07156/) (2023)
- [Efficiently solving the thief orienteering problem with a max-min ant colony optimization approach](https://mlsanity.com/paper/2109.13103/) (2021)
- [On the Fitness Landscapes of Interdependency Models in the Travelling Thief Problem](https://mlsanity.com/paper/2203.04363/) (2022)

## Abstract (arXiv)

Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a very promising trajectory as advances in these problem-solving approaches could result in adding values to major industries. In this paper we revisit this trajectory to find out whether the attempts that started three decades ago are still aligned with the same goal, as complexities of real-world problems increased significantly. We present some examples of modern real-world problems, discuss why they might be difficult to solve, and whether there is any mismatch between these examples and the problems that are investigated in the evolutionary computation area.