Golomb Rulers: the Influence of Representation and Heuristics



Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem.

Several representations for the Optimal Golomb Ruler problem are examined. Common mutation operators such as bit-flip mutation and classic one point and uniform crossover are employed to generate fitness landscapes to study the genetic representations. Furthermore, additional experiments are made to observe the effects of adding heuristics and local improvements to the encodings.

The fitness landscape analysis was complemented with a statistical analysis. Several hypothesis tests were used in order to verify and support the previous findings. The research is conclued by relating the analysis with results obtained from optimization runs and additional experimentation concerning insertion and correction procedures.


Evolutionary Optimization

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