Engineering a Memetic Algorithm from Discrete Cuckoo Search and Tabu Search for Cell Assignment of Hybrid Nanoscale CMOL Circuits
Bibliographic record
Abstract
Cuckoo search optimization (CSO) algorithm, a recently proposed metaheuristic, has shown promising results in various problem domains. Results from recent studies show that engineering and tuning discrete cuckoo search optimization’ parameters is a daunting task. In this paper, an attempt to enhance the performance of the CSO algorithm in solving discrete combinatorial optimization problems is presented. Performance of the discrete modified CSO algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), hybrid of GA/PSO, and simulated annealing. In addition, a memetic algorithm (MA) that combines discrete modified CSO and tabu search is proposed. Results show that the proposed improvements help in enhancing the performance of the original algorithm. As a test case, the NP-hard problem of buffer minimization in CMOL (CMOS[Formula: see text]+[Formula: see text]nanowire[Formula: see text]+[Formula: see text]MOLecules) circuits is addressed. The performance of the proposed implementation of CSO algorithm is compared with other heuristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".