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Automating the packing heuristic design process with genetic programming.

Edmund K Burke¹, Matthew R Hyde, Graham Kendall

¹University of Nottingham, School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB, UK. ekb@cs.nott.ac.uk

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|May 26, 2011

Summary

This summary is machine-generated.

This study introduces a genetic programming system that automates the design of heuristics for complex packing problems. The system generates competitive packing algorithms, demonstrating human-level performance across various problem types.

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Area of Science:

Operations Research
Computer Science
Artificial Intelligence

Background:

Bin packing and knapsack packing problems are computationally difficult in operational research.
Existing exact, heuristic, and metaheuristic methods often lack generality, requiring specific tuning for different problem instances.
The selection of an appropriate method for a given packing problem instance is often unclear.

Purpose of the Study:

To automate the design of heuristic methods for packing problems.
To develop a generalizable approach for heuristic design applicable across various packing problem dimensions and types.
To demonstrate that computer-designed heuristics can achieve human-competitive performance.

Main Methods:

A genetic programming system was developed to automatically generate heuristics.
The methodology was applied without modification to one-, two-, and three-dimensional bin packing and knapsack packing problems.
Extensive experiments were conducted to compare the system's performance against human-designed heuristics.

Main Results:

The genetic programming system successfully generated high-quality heuristics for diverse packing problems.
The automated heuristic design methodology demonstrated a high level of generality, outperforming specialized human-designed heuristics.
The system achieved human-competitive results across a wide range of packing domains.

Conclusions:

Automating heuristic design through genetic programming offers a powerful and generalizable approach to solving complex packing problems.
The proposed methodology provides a novel and effective alternative to traditional, manually designed heuristics.
This work represents a significant advancement in automated algorithm design for operational research challenges.