Towards the Design of Heuristics by Means of Self-Assembly

TL;DR

This paper proposes a novel, nature-inspired methodology for automatically designing heuristics using self-assembly processes, aiming to improve the automation and effectiveness of heuristic generation for problem-solving.

Contribution

It introduces a new approach leveraging self-assembly principles for heuristic design, expanding the methods available for automated, problem-specific heuristic creation.

Findings

Demonstrates the feasibility of self-assembly-based heuristic design

Shows potential for generating effective problem-specific heuristics

Provides a foundation for further research in autonomous heuristic construction

Abstract

The current investigations on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for the problem at hand. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. Some approaches like genetic programming have been proposed for this. In this paper, we explore an elegant nature-inspired alternative based on self-assembly construction processes, in which structures emerge out of local interactions between autonomous components. This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly.