- author = "Julian A. Forrester",
- title = "Strongly Typed Cartesian Genetic Programming and its applications",
- school = "University of Essex",
- year = "2026",
- address = "UK",
- month = feb,
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, STGP, machine learning, computer vision, strong typing",
-
URL = "
https://repository.essex.ac.uk/42791/",
-
URL = "
https://repository.essex.ac.uk/42791/1/Thesis_final_corrected.pdf",
-
DOI = "
10.5526/ERR-00042791",
- size = "196 pages",
-
abstract = "Genetic Programming (GP) and its graph-based variant,
Cartesian Genetic Programming (CGP), have proved highly
effective machine-learning frameworks that can evolve
human-readable programs that can match or exceed
hand-crafted solutions across many tasks. Strongly
Typed Cartesian Genetic Programming (ST-CGP) extends
CGP by assigning explicit data-types to every input,
output and operator, and allowing features to have
varying arities. These type constraints prune
infeasible regions of the search space while preserving
CGP directed-acyclic-graph representation and
single-row genome, leading to faster, semantically
correct evolution. Unlike standard CGP, ST-CGP also
introduces two forms of crossover-full two-point
recombination and genetic rewiring; providing a second
source of variation that is rarely available in
conventional CGP frameworks.
Because operators are typed, ST-CGP can be rapidly retargeted: numeric, Boolean and higher-level domain primitives (e.g. OpenCV filters) can coexist in a single run, enabling one framework to span diverse problem domains. This versatility is illustrated in this thesis by three application areas. In computer vision, ST-CGP evolved segmentation, detection and classification pipelines that solved benchmark object-sorting problems and achieved convolutional-neural-network-level accuracy on a 27000-image malaria-cell dataset with far smaller training sets and CPU-only resources. In agriculture, it classified field parcels into low, high and reference yield zones using laboratory soil measurements with competitive accuracy and markedly low variance relative to traditional models. Finally, it learned predictive models mapping five-minute VOC gas fingerprints from an electronic-nose sensor to multiple soil health indicators, delivering laboratory-grade predictions, an application which has now been adopted by UK agronomists in commercial practice.
Collectively, these results demonstrate that the combination of strong typing, an enriched operator palette and novel crossover elevates CGP to a general-purpose, interpretable evolutionary programming system capable of tackling data-rich tasks from medical imaging to environmental sensing within a single unified framework.",
- notes = "Supervisor: Adrian Clark and Renato Amorim",
