Towards a White Box Approach to Automated Algorithm Design
Authors: Steven Adriaensen, Ann Nowé
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate some of the benefits of white box evaluation. To this purpose, we compare the performance of the implementation described in Section 5 to that of a similar black box implementation on two micro-benchmarks. These implementations differ in that the white box optimizer (WB) maintains transition data (n,r) and returns , while the black box optimizer (BB) maintains a c ! f(c) mapping and returns c = arg maxc0 f(c0). Figures 2 and 4 show the performance of the algorithm returned by each optimizer, after x algorithm evaluations,2 averaged over 100 independent meta-optimization runs. |
| Researcher Affiliation | Academia | Steven Adriaensen, Ann Nowe Vrije Universiteit Brussel Pleinlaan 2, 1050 Elsene, Belgium {steven.adriaensen, ann.nowe}@vub.ac.be |
| Pseudocode | Yes | Figure 1: Code for Benchmark 1 |
| Open Source Code | Yes | We have implemented our optimizer as a standalone Java Library.1 https://github.com/Steven-Adriaensen/White-box-ADP |
| Open Datasets | No | The paper introduces two 'micro-benchmarks' defined by code snippets within the paper (Figure 1 and Figure 3) rather than using external, publicly available datasets with specific access information or standard citations. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing. It refers to 'a given input (with variable seed)' for evaluations but not formal splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions that the optimizer is implemented as a 'standalone Java Library' but does not specify a version number for Java or any other software dependencies with their respective versions. |
| Experiment Setup | No | The paper describes the general approach of the solver and the agents used (URS, PURS, GR) but does not provide specific experimental setup details such as hyperparameters (e.g., learning rates, batch sizes, specific numerical settings for the optimizer) typically found in reproducible experimental setups. |