Differential Evolution on a GPGPU: The Influence of Parameters on Speedup and the Quality of Solutions
Bibliographic record
Abstract
One challenge in studying the speedup performance of evolutionary optimization techniques, particularly in differential evolution, is that many parameters including crossover rate, F, dimensionality, population size and the complexity of the objective function play an important role. In fact, these same parameters also effect the quality of the obtained results. Therefore, it is important to understand the interaction between these parameters in order to make good choices for these key parameters that drive both the quality and speedup metrics. Thus, the purpose of this paper is to show how parameters such as crossover rate, F, dimension, population size, and calls to evaluation functions can influence the speedup and the quality of solutions in a differential evolution algorithm in high dimension problems. The evaluation was done using a 2^k factorial analysis considering a Schwefel Benchmark Function in a Mat lab implementation running on a general purpose GPU. Results have shown that a reasonable speedup can be reached taking into account a high level of programming, i.e., There are a good trade-off between the required effort to program on GPU in Mat lab and the reached Speedup. On the other hand, results in terms of quality of solutions showed that CPU tends to produce better outcomes in some configurations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".