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On the Performance of Different Genetic Programming Approaches for the SORTING Problem.

Markus Wagner¹, Frank Neumann², Tommaso Urli³

¹Optimisation and Logistics, University of Adelaide, Adelaide, Australia markus.wagner@adelaide.edu.au.

Evolutionary Computation

|April 15, 2015

Summary

This summary is machine-generated.

Genetic programming bloat, where solutions grow excessively, hinders optimization. This study analyzes algorithms on the sorting problem to understand and bound bloat, improving evolutionary algorithms.

Keywords:

Computational complexity genetic programming multiobjective optimization single-objective optimization sortedness variable-length representation

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Area of Science:

Computer Science
Artificial Intelligence
Evolutionary Computation

Background:

In genetic programming, solution size is flexible but can lead to "bloat," where individuals grow without added benefit, impeding optimization.
Bloat can prevent variable-length evolutionary algorithms from solving even relatively simple problems.

Purpose of the Study:

To analyze single- and multiobjective algorithms on the sorting problem, which lacks typical fitness structures.
To investigate the phenomenon of bloat in genetic programming and its impact on optimization.

Main Methods:

Analysis of different single- and multiobjective algorithms.
Experimental evaluation on the sorting problem, chosen for its lack of independent and additive fitness structures.
Complementing theoretical results with comprehensive experiments.

Main Results:

The study provides insights into the performance of various algorithms on a problem susceptible to bloat.
Experimental results help to validate existing theoretical bounds related to bloat.
Identified areas where theoretical bounds for bloat are currently missing.

Conclusions:

Understanding and bounding bloat is crucial for improving the efficiency of genetic programming.
The sorting problem serves as a valuable test case for analyzing bloat in evolutionary algorithms.
This research contributes to the development of more robust and effective variable-length evolutionary algorithms.