Mendelian and Non-Mendelian \nAncestral Repair for Constrained \nEvolutionary Optimisation
Bibliographic record
Abstract
Evolutionary Algorithms (EA) are excellent at solving many types of problems \nbut are inherently ill-suited to solving constrained problems. Previously \nthere has been four ways to adapt these algorithms to solve constrained \nproblems - pareto optimal strategies, modified representation and operators, \npenalty functions and repair strategies. This thesis makes significant contributions \nto the topic of genetic repair and introduces a non-Mendelian repair \noperator that has been inspired by a naturally occurring genetic repair mechanism \nin the Arabidopsis thaliana plant. Thus, the analogy between EA and \nnatural evolution is extended to incorporate this (still highly controversial) \nbiological repair process. \nThe first and main objective focuses on Evolutionary Algorithms. This \nthesis adapts this novel genetic repair strategy to an EA to solve two benchmark \nconstraint based problems - specifically permutation problems as this \ncategory of problem are often recognised as the most problematic problems \nfor the canonical EA to deal with. \nThe second objective was more biological, relating to Evolutionary Algorithms. \nA number of algorithmic and parametric interventions were made \nto the EA, to examine the repair algorithm’s performance under more biologically \ninspired conditions. \nThis thesis illustrates that non-Mendelian ancestral repair templates outperform \ntheir Mendelian counterparts under a wide variety of conditions and \nalso shows that under biologically inspired conditions, the non-Mendelian \nrepair strategy continues to outperform its Mendelian counterpart.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".