Genetic Algorithm with Hybrid Integer Linear Programming Crossover Operators for the Car-Sequencing Problem
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Bibliographic record
Abstract
In this paper, we present three new integrative approaches for solving the classical car-sequencing problem. These hybrid approaches are essentially based on a genetic algorithm which incorporates crossover operators using an integer linear programming model during the crossover process for the construction of a solution. This form of integrative hybridization has been proposed by Cotta and Troya in a framework for hybridizing evolutionary algorithms with a branch-and-bound algorithm in order to explore the dynastic potential of the two parents' solutions and thus obtain the best offspring. However, while our crossovers also use problem-knowledge in the recombination process, they are not strictly transmitting operators and do not limit the exploration to the dynastic potential of the parents' solutions. We show that the hybrid approach outperforms a genetic algorithm with local search and other algorithms found in the literature on the CSPLib benchmarks. Although the computation times are long when integrative hybridization is used, this study well illustrates the interest of designing hybrid approaches exploiting the strengths of different 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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