Multi-Objective Coordinate Search Optimization
Bibliographic record
Abstract
Many real-world optimization problems can be modelled with several competing objectives. Most of the time, such optimization problems fall under the category of expensive problems. These are problems in which each fitness evaluation is time-consuming, for example, one fitness call could take hours or even days to compute. The time-consuming process of evaluating the function values or gradient of the objective functions may degrade the running speed of many optimization algorithms. The coordinate search (CS) approach is introduced as a single-objective gradient-free method for addressing large-scale, non-convex, and costly optimization problems. Due to the low computation and memory requirements of the CS algorithm, it can also be efficiently extended to address multi-objective optimization problems. The subject of this study is to develop a CS-based algorithm aimed at computationally expensive multi-objective optimization problems. In order to generate a set of non-dominated solutions, a population is created to apply the CS algorithm on each individual and finally reach an optimized interval. We demonstrate the efficacy of the proposed multi-objective CS method by comparing it with NSGA-II and MOEA/D as one of the well-known multi-objective algorithms on ZDT benchmark functions. Promising results are reported with the assumption of a limited number of fitness evaluations (NFF) which is desired during tackling complex and expensive optimization problems. Another major advantage of the proposed algorithm is that it provides regions of the Pareto front, that using sampling can generate as many Pareto front solutions as needed unlike other common optimization algorithms including NSGA-II.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".