relwork: implemented Kronberger feedback

thesis/references.bib

@@ -1164,3 +1164,61 @@
 	author = {Palacios, Jonathan and Triska, Josh},
 	date = {2011},
 }
+
+@inproceedings{gustafson_improving_2005,
+	title = {On improving genetic programming for symbolic regression},
+	volume = {1},
+	url = {https://ieeexplore.ieee.org/abstract/document/1554780},
+	doi = {10.1109/CEC.2005.1554780},
+	abstract = {This paper reports an improvement to genetic programming ({GP}) search for the symbolic regression domain, based on an analysis of dissimilarity and mating. {GP} search is generally difficult to characterise for this domain, preventing well motivated algorithmic improvements. We first examine the ability of various solutions to contribute to the search process. Further analysis highlights the numerous solutions produced during search with no change to solution quality. A simple algorithmic enhancement is made that reduces these events and produces a statistically significant improvement in solution quality. We conclude by verifying the generalisability of these results on several other regression instances},
+	eventtitle = {2005 {IEEE} Congress on Evolutionary Computation},
+	pages = {912--919 Vol.1},
+	booktitle = {2005 {IEEE} Congress on Evolutionary Computation},
+	author = {Gustafson, S. and Burke, E.K. and Krasnogor, N.},
+	date = {2005-09},
+	keywords = {Computer science, Concrete, Diversity methods, Evolutionary computation, Genetic programming, Problem-solving},
+	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\28ZEEUYG\\Gustafson et al. - 2005 - On improving genetic programming for symbolic regression.pdf:application/pdf},
+}
+
+@incollection{korns_extremely_2015,
+	location = {Cham},
+	title = {Extremely Accurate Symbolic Regression for Large Feature Problems},
+	isbn = {978-3-319-16030-6},
+	url = {https://doi.org/10.1007/978-3-319-16030-6_7},
+	abstract = {grammarnonlinear regressiongeneralized linear models ({GLM})basis functionmaximum binary {treeRegression} Query Language ({RQL})islandelitistconstraintextreme accuracystepwise regressionheuristicridge {regressionpolynomialAsKorns} Michael F.symbolic regression ({SR}) has advanced into the early stages of commercial exploitation, the poor accuracy of {SR}, still plaguing even the most advanced commercial packages, has become an issue for early adopters. Users expect to have the correct formula returned, especially in cases with zero noise and only one basis function with minimally complex grammar depth.},
+	pages = {109--131},
+	booktitle = {Genetic Programming Theory and Practice {XII}},
+	publisher = {Springer International Publishing},
+	author = {Korns, Michael F.},
+	editor = {Riolo, Rick and Worzel, William P. and Kotanchek, Mark},
+	date = {2015},
+	langid = {english},
+}
+
+@misc{bruneton_enhancing_2025,
+	title = {Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired Constraints},
+	url = {https://doi.org/10.48550/arXiv.2503.19043},
+	doi = {10.48550/arXiv.2503.19043},
+	abstract = {This paper presents {QDSR}, an advanced symbolic Regression ({SR}) system that integrates genetic programming ({GP}), a quality-diversity ({QD}) algorithm, and a dimensional analysis ({DA}) engine. Our method focuses on exact symbolic recovery of known expressions from datasets, with a particular emphasis on the Feynman-{AI} benchmark. On this widely used collection of 117 physics equations, {QDSR} achieves an exact recovery rate of 91.6{\textasciitilde}\${\textbackslash}\%\$, surpassing all previous {SR} methods by over 20 percentage points. Our method also exhibits strong robustness to noise. Beyond {QD} and {DA}, this high success rate results from a profitable trade-off between vocabulary expressiveness and search space size: we show that significantly expanding the vocabulary with precomputed meaningful variables (e.g., dimensionless combinations and well-chosen scalar products) often reduces equation complexity, ultimately leading to better performance. Ablation studies will also show that {QD} alone already outperforms the state-of-the-art. This suggests that a simple integration of {QD}, by projecting individuals onto a {QD} grid, can significantly boost performance in existing algorithms, without requiring major system overhauls.},
+	number = {{arXiv}:2503.19043},
+	publisher = {{arXiv}},
+	author = {Bruneton, J.-P.},
+	date = {2025-03-24},
+	keywords = {Computer Science - Neural and Evolutionary Computing, Computer Science - Symbolic Computation, Physics - Data Analysis, Statistics and Probability},
+	file = {Preprint PDF:C\:\\Users\\danwi\\Zotero\\storage\\9U346ZEV\\Bruneton - 2025 - Enhancing Symbolic Regression with Quality-Diversity and Physics-Inspired Constraints.pdf:application/pdf},
+}
+
+@incollection{knuth_mmix_1999,
+	location = {Berlin, Heidelberg},
+	title = {{MMIX}},
+	isbn = {978-3-540-46611-6},
+	url = {https://doi.org/10.1007/3-540-46611-8_2},
+	abstract = {Thirty-eight years have passed since the {MIX} computer was designed, and computer architecture has been converging during those years towards a rather different style of machine. Therefore it is time to replace {MIX} with a new computer that contains even less saturated fat than its predecessor.},
+	pages = {2--61},
+	booktitle = {{MMIXware}: A {RISC} Computer for the Third Millennium},
+	publisher = {Springer},
+	author = {Knuth, Donald E.},
+	editor = {Knuth, Donald E.},
+	date = {1999},
+	langid = {english},
+}