cs.AIMar 26, 2026

EvoForest: A Novel Machine-Learning Paradigm via Open-Ended Evolution of Computational Graphs

Authors: Kamer Ali Yuksel, Hassan Sawaf

Organizations: aiXplain, Inc., San Jose, CA, USA

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Abstract

Modern machine learning is still largely organized around a single recipe: choose a parameterized model family and optimize its weights. Although highly successful, this paradigm is too narrow for many structured prediction problems, where the main bottleneck is not parameter fitting but discovering what should be computed from the data. Success often depends on identifying the right transformations, statistics, invariances, interaction structures, temporal summaries, gates, or nonlinear compositions, especially when objectives are non-differentiable, evaluation is cross-validation-based, interpretability matters, or continual adaptation is required. We present EvoForest, a hybrid neuro-symbolic system for end-to-end open-ended evolution of computation. Rather than merely generating features, EvoForest jointly evolves reusable computational structure, callable function families, and trainable low-dimensional continuous components inside a shared directed acyclic graph. Intermediate nodes store alternative implementations, callable nodes encode reusable transformation families such as projections, gates, and activations, output nodes define candidate predictive computations, and persistent global parameters can be refined by gradient descent. For each graph configuration, EvoForest evaluates the discovered computation and uses a lightweight Ridge-based readout to score the resulting representation against a non-differentiable cross-validation target. The evaluator also produces structured feedback that guides future LLM-driven mutations. In the 2025 ADIA Lab Structural Break Challenge, EvoForest reached 94.13% ROC-AUC after 600 evolution steps, exceeding the publicly reported winning score of 90.14% under the same evaluation protocol.

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Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 1: EvoForest architecture. EvoForest DAG (left), two-phase evaluation pipeline (center), LLM-guided scientist/engineer mutation loop (right), and asynchronous island model (bottom).
Entry / systemCategoryDescription / relevanceScore
EvoForest (ours)Search-firstMulti-alternative DAG with reusable computations, callable families, trainable globals, configuration search, and diagnostic-guided LLM mutation.94.13
alphabotChallenge winnerTop public entry on the final leaderboard; strongest direct reference.90.14
homely-ratIndustry entryWeco AI using AIDE , an autonomous ML competition agent.78.32
Appendix
Table 1: Benchmark comparison for the ADIA structural-break task. The public leaderboard provides the most direct external benchmark for this live competition. We also include the Weco AI leaderboard entry as a baseline because they publicly introduced AIDE , an autonomous competition agent; independent evaluation in MLE-bench characterizes AIDE as a Kaggle-specialized tree-search scaffold.
Figure 2: Optimization and search-space diagnostics. (a) Best-so-far ROC–AUC. (b) Graph complexity over time. (c) Per-island scores. (d) Configuration count.
Figure 3: Structural and systems diagnostics. (a) Score vs. complexity. (b) Structural changes. (c) Acceptance rates. (d) Wall-clock time.
Figure 4: Additional diagnostics. (a) Discoveries by island. (b) Best–mean gap. (c) Score statistics. (d) Score variance.
VersionStepIslandAUCΔ\Delta AUCPhase / key innovation
v00110-30.6120—Bootstrap. LLM generated seed; jump/local-MAD ratio and variance-jump interaction establish the initial pre/post contrast basis.
v005810.6418+0.0298Bootstrap. First strong jump from skewness/kurtosis contrasts, jump-absolute features, and a soft-gated nonlinear pathway.
v0091820.6506+0.0088Bootstrap. Spectral-entropy pairing, FFT-slope fusion, and mean–std interaction improve coarse segment discrimination.
v0144200.6640+0.0134Early feature expansion. Spectral entropy difference, Gaussian KL divergence, and an early MLP-style gate enrich the feature family.
v0187420.6726+0.0086Early feature expansion. Multi-scale windowed variance ratio, refined pruning, and CUSUM-conditioned gating improve robustness.
v02211120.6789+0.0063Early feature expansion. Joint gating, mixed RBF-style similarity, and higher-order moment contrasts expand the candidate family.
Appendix
Table 2: Global-best milestones for the long-horizon ADIA run.
ComparisonDimsMean Acc.W / T / L
MSRF full1,4100.808—
MiniRocket10,0000.79459 / 14 / 39
MiniRocket+MSRF10,4100.80367 / 31 / 14 ∗
∗ vs. MiniRocket alone
Appendix
Table 3: MSRF on the 112-dataset UCR archive. W/T/L counts are relative to each baseline.
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