Heider, Michael
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Year of publication
- 2024 (4) (remove)
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- English (4)
Keywords
- CGP (1)
- Cartesian Genetic Programming (1)
- Crossover (1)
- Positional Bias (1)
- Recombination (1)
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The Machine Health (MH) sector—which includes, for example, Predictive Maintenance, Prognostics and Health Management, and Condition Monitoring—has the potential to improve efficiency and reduce costs for maintenance and machine operation. This is achieved by data-driven analytics applications, utilising the vast amount of data collected by sensors during machine runtime. While there are numerous possible fields of application, the overall complexity of machines and applications in scientific publications is still low, preventing MH technologies from being implemented in many real-world scenarios. This may be the result of a diffuse understanding of the term complexity in the publications of this field, which results in a lack of focus towards the core problems of real-world MH applications. This article introduces a new way of discerning complexity in data-driven MH applications, enabling an effective discussion and analysis of present and future MH applications. This is achieved by creating a new taxonomy based on observations from relevant literature and substantial domain knowledge. Using this newly introduced taxonomy, we categorise recent applications of MH to demonstrate the usefulness of our approach and illustrate a still-prevalent research gap based on our findings.
The recombination operator plays an important role in many evolutionary algorithms. However, in Cartesian Genetic Programming (CGP), which is part of the aforementioned category, the usefulness of crossover is contested. In this work, we investigate whether CGP’s positional bias actually influences the usefulness of the crossover operator negatively. This bias describes a skewed distribution of CGP’s active and inactive nodes, which might lead to destructive behaviours of standard recombination operators. We try to answer our hypothesis by employing one standard CGP implementation and one without the effects of positional bias. Both versions are combined with one of four standard crossover operators, or with no crossover operator. Additionally, two different selection methods are used to configure a CGP variant. We then analyse their performance and convergence behaviour on eight benchmarks taken from the Boolean and symbolic regression domain. By using Bayesian inference, we are able to rank them, and we found that positional bias does not influence CGP with crossover. Furthermore, we argue that the current research on CGP with standard crossover operators is incomplete, and CGP with recombination might not negatively impact its evolutionary search process. On the contrary, using CGP with crossover improves its performance.
