Improving the search pattern of Rooted Tree Optimisation algorithm to solve complex problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rooted Tree Optimisation (RTO) is a metaheuristic method for solving complex problems. Roots in RTO move to new search space without evaluating such move for better fitness values. Best root in RTO which acts similar to the global best in Particle Swarm Optimisation, is used to influence the movement of the rest of the roots to a promising region. This influence may lead the algorithm to premature convergence. The Lateral Growth Rooted Tree Optimisation (LGRTO) that is proposed in this work eliminates the influence of the best root to direct the search pattern of other roots. This is achieved by introducing hydrotropism and lateral growth equations. In both equations, the influence of the best root is eliminated, and every exploitation or exploration is evaluated before the roots move to new points in the search space. Three types of experiments were done. Two of these experiments are benchmark functions for continuous optimisation problems. The third type is a fourth order Butterworth filter design problem. The result of the nonparametric test of experiments on continuous optimisation problem indicated that LGRTO obtained better performance over Rooted Tree Optimisation and Real Coded Genetic Algorithm. The performance of LGRTO is competitive with the result of Teaching and Learning Based Optimisation (TLBO) and LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES (LSHADE-SPACMA) methods. For the result of Butterworth design problem, LGRTO obtained a design error that is lower than the value obtained by RTO, Particle Swarm Optimisation (PSO), Differential Evolution (DE), Tabu Search (TS), and Artificial Bee Colony (ABC) methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it