REINSURANCE ANALYTICS USING SERIAL AND PARALLEL COMPUTATION ON THE MULTIOBJECTIVE EVOLUTIONARY ALGORITHM SPEA2
Bibliographic record
Abstract
This paper presents a novel and efficient application of the SPEA2 on reinsurance contract optimization considering the perspective from an insurance company. The reinsurance operation aims to transfer the risk taken by an insurance company, usually against natural catastrophes, to a bigger corporation. The process of reinsurance is similar to that one where a client wants to insure his properties upon the payment of a premium. Then, the insurance company sells his portfolio using the reinsurance market aiming to maximize the expected return and, at the same time, to maximize the risk hedged to the reinsurance company. This problem is naturally multi-objective, consequently the SPEA2 algorithm appears as an attractive approach to tackle the problem. Results show that the SPEA2 can obtain better outcomes than a sophisticated algorithm called enhanced MO-PBIL in terms of hypervolume. A parallel version based on the master-slave model showed that a speedup of 1.83 can be reached using 4 cores and 500 iterations, and 2.13 using 8 cores and 100 iterations, with little effort to parallelize the application.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".