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An empirical comparison of backtracking algorithms.

C A Brown¹, P W Purdom

¹Department of Computer Science, Indiana University, Bloomington, IN 47405.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 27, 2011

Summary

This study compares search rearrangement backtracking methods. Zero-level backtracking is optimal for small problems, one-level for moderate, and two-level for large problems, guiding method selection.

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Area of Science:

Computer Science
Artificial Intelligence
Algorithms

Background:

Search rearrangement backtracking is crucial for solving complex problems.
Different levels of backtracking (zero, one, and two) offer varying performance characteristics.

Purpose of the Study:

To experimentally evaluate and compare the efficiency of zero-level, one-level, and two-level search rearrangement backtracking methods.
To establish clear guidelines for selecting the most effective backtracking method based on problem size.

Main Methods:

Conducted experimental studies on zero-level, one-level, and two-level search rearrangement backtracking.
Determined upper and lower limits for problem sizes where each method is preferred.
Developed theoretical asymptotic formulas to complement experimental findings.

Main Results:

Zero-level backtracking is most efficient for very small problems.
One-level backtracking excels in moderate-sized problems.
Two-level backtracking is superior for extremely large problems.

Conclusions:

The choice of backtracking method significantly impacts performance based on problem scale.
Experimental data and theoretical formulas provide a practical guide for optimizing search rearrangement strategies.