AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation

Tai Nguyen; Fei Liu; Phong Le; Carola Doerr; Nguyen Dang

cs.LGOct 2, 2026

AdaEva: Accelerating LLM-Driven Algorithm Design with Adaptive Partial Evaluation

Authors: Tai Nguyen, Fei Liu, Phong Le, Carola Doerr, Nguyen Dang

Organizations: University of St Andrews, United Kingdom · Sorbonne Universit´e, CNRS, LIP6, France · University of Zurich and ETH Zurich, Switzerland

Abstract

Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms can be excessive. We consider the common LLM-driven automated algorithm design (LLM4AD) setting in which a candidate algorithm is evaluated by aggregating its performance over a shared set of training instances. This instance-wise structure raises a natural question: must every candidate be evaluated on the entire instance set before deciding whether it remains competitive? Taking inspiration from algorithm configuration, we introduce AdaEva, a drop-in adaptive partial-evaluation framework that progressively evaluates candidates on larger subsets of the same instance pool and eliminates unpromising candidates as evidence accumulates. Importantly, AdaEva leaves the underlying LLM4AD procedure and per-instance evaluator unchanged and requires no prior knowledge about instance difficulty. We instantiate this idea using successive halving (AdaEva-S) and statistical racing (AdaEva-R), and evaluate both mechanisms across three representative LLM4AD frameworks, multiple LLM backbones, and optimization domains spanning combinatorial and continuous black-box optimization. Under matched evaluation budgets, AdaEva more reliably balances evaluation effort across candidates than fixed partial-evaluation strategies, yielding strong search efficiency and anytime performance together with improved held-out generalization across the evaluated settings.

Figures & tables

Figure 1: LLM-driven algorithm design as a loop. At each generation, a prompt built from the problem specification and selected parents is passed to an LLM, which generates candidate algorithms; these are evaluated on problem instances, and the best are selected and fed back to guide the next generation. Standard frameworks evaluate every candidate on every instance (left). AdaEva replaces only the evaluation stage with adaptive partial evaluation (right): candidates are evaluated progressively, those that fall behind are eliminated early, and the saved budget is concentrated on the candidates that remain competitive. Prompt construction, generation, and selection are unchanged.

Framework	Problem	Search Pool	Held-out Set	$M$	$N$	$T$	$B$
EoH	OBP	5,000 items, capacity 100	1k/5k/10k items, capacities 100/500	20	20	20	10,000
	TSP	100-city, uniform coordinates	29 TSPLIB instances	10	10	10	6,400
	FSSP	50 jobs, 2–20 machines	Taillard benchmark	10	10	10	6,400
HiFo-Prompt	OBP	5,000 items, capacity 100	1k/5k/10k items, capacities 100/500	10	50	10	12,500
	TSP	100-city, uniform coordinates	29 TSPLIB instances	10	50	5	16,000
	FSSP	50 jobs, 2–20 machines	Taillard benchmark	10	50	5	16,000

Table 1: Benchmark and search settings including the search and held-out instance sets, population size

M

, offspring generated per generation

N

, number of generations

T

, and evaluation budget

B

Framework	Benchmark	LLM	Fixed-1	Fixed-25%	Fixed-50%	Full	AdaEva-S	AdaEva-R
EoH	FSSP	Devstral	4.967	3.167	3.767	3.933	3.133	2.033
	FSSP	Qwen	4.767	4.033	2.667	4.267	2.867	2.400
	OBP	Devstral	2.717	1.817	3.067	5.083	3.667	4.650
	OBP	Qwen	1.983	4.300	3.567	3.867	3.400	3.883
	TSP	Devstral	4.333	2.967	3.133	6.000	2.900	1.667
	TSP	Qwen	3.933	2.767	2.933	5.833	3.367	2.167

Table 2: Held-out generalization performance of each evaluation strategy across LLM4AD frameworks, benchmarks, and LLM backbones. Mean ranks (lower is better) are computed across held-out instance group, then averaged across checkpoints. Bold and underlined denote the best and second-best methods in each row, respectively. Detailed per-checkpoint and third LLM backbone (Gemini) results are provided in Appendix C .

Figure 2: Held-out generalization trajectories of evaluation strategies across three frameworks. Mean ranks (lower is better) are computed within each held-out instance group, then averaged across the corresponding benchmarks, and LLM backbones. Each panel shows performance at increasing fractions of the search budget (20%–100%).

Figure 3: Search performance under increasing evaluation budget with the Devstral backbone, on 3 settings chosen to span the range of behaviours observed. Incumbents are selected during search from partial evaluations and then re-evaluated on the complete pool for reporting; the y-axis gives that value, as optimality gap for OBP, gap for TSP, and mean AOCC for BBOB; lower is better in the first two panels and higher is better in the third. Curves show the mean over 5 runs, with shaded regions indicating variability across seeds. The remaining 4 settings, and results with the Qwen2.5-Coder and Gemini-3.1 backbones are available in Appendix C .

Benchmark	Kendall’s $W$	Across CV	Within CV	Mean Depth	Stop at First Block
	Property			AdaEva-R Depth
OBP	1.0000	0.3403	0.0016	$7.04\pm 0.72$	$92.8\%\pm 3.5\%$
TSP	0.0312	0.0001	0.0348	$12.49\pm 1.98$	$73.8\%\pm 5.6\%$
FSSP	0.6133	0.0088	0.1216	$19.25\pm 1.02$	$46.1\%\pm 4.4\%$
BBOB	0.6567	0.6809	1.1235	$22.79\pm 2.94$	$61.9\%\pm 4.4\%$

Table 3: Benchmark properties and the evaluation depth reached by AdaEva-R. Kendall’s

W

and the Across- and Within-candidate coefficients of variation (CVs) are computed from a fixed initial set of candidates evaluated on every instance. Evaluation depth is reported under EoH for the CO benchmarks and under LLaMEA for BBOB. Definitions of these metrics are provided in Section C.1 .

Strategy	Configuration	EoH/OBP	EoH/TSP	EoH/FSSP	LLaMEA/BBOB
AdaEva-S	$\underline{n}_{0}=10$	$+1.41$	$-0.17$	$-0.02$	$-2.01$
AdaEva-S	$\eta=1.5$	$+0.25$	$+0.26$	$-0.02$	$-7.74$
AdaEva-R	$\mathcal{T}_{\mathrm{stats}}=t$ -test	$+2.39$	$+0.04$	$-0.06$	$+14.93$
	Refill = Off	$-0.39$	$+0.03$	$-0.01$	$+19.48$
	Stochastic = Off	$+0.41$	$+0.09$	$+0.03$	$+21.16$
	Prompt = Partial Evaluation	–	–	–	$+14.99$

Table 4: Selected sensitivity results. Each value is the percent change in incumbent quality relative to the default of the same instantiation, computed within matched seeds, taking the median across seeds at each of five budget checkpoints and then averaging across checkpoints. Positive values indicate worse performance than the default. The complete sweep is reported in Appendix E .

Figure 4: Relative number of candidate algorithms evaluated during the search, under different evaluation strategies across EoH, HiFo-Prompt, and LLaMEA (using the Devstral backbone). The values are normalized by the corresponding full-evaluation baseline, so larger ratios indicate that more candidate algorithms can be sampled within the same evaluation budget; error bars denote variability across five runs.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Search performance under increasing evaluation budget with the Devstral backbone for HiFo-Prompt. Each panel compares AdaEva-S and AdaEva-R with Fixed-

K

evaluation at

K=1

25\%

, and

50\%

of the pool, and with full-pool evaluation (

K=|\mathcal{I}|

). Incumbents are selected during search from partial evaluations and re-evaluated on the complete pool for reporting. The y-axis shows this full-pool performance, measured as optimality gap for OBP and TSP and makespan for FSSP, with lower values better. Curves show mean performance across five independent search seeds, with shaded regions indicating variability across seeds.

Panel of Figure 6 — Figure 6: Search performance under increasing evaluation budget with the Qwen2.5-Coder backbone for EoH (top) and HiFo-Prompt (bottom). Each panel compares AdaEva-S and AdaEva-R with Fixed- $K$ evaluation at $K=1$ , $25\%$ , and $50\%$ of the pool, and with full-pool evaluation ( $K=|\mathcal{I}|$ ). Incumbents are selected during search from partial evaluations and re-evaluated on the complete pool for reporting. The y-axis shows this full-pool performance, measured as optimality gap for OBP and TSP and makespan for FSSP, with lower values better. Curves show mean performance across five independent search seeds, with shaded regions indicating variability across seeds.

Figure 7: Search performance under increasing evaluation budget with the Qwen2.5-Coder and Gemini-3.1-Flash-Lite backbones. The LLaMEA/BBOB panels and the EoH/TSP panel compare AdaEva-S and AdaEva-R with Fixed-

K

evaluation at

K=25\%

and

50\%

of the pool, and with full-pool evaluation (

K=|\mathcal{I}|

). Incumbents are selected during search from partial evaluations and re-evaluated on the complete pool for reporting. The y-axis shows mean AOCC for BBOB and optimality gap for TSP; higher AOCC and lower gap indicate better performance. Curves show mean performance across five independent search seeds, with shaded regions indicating variability across seeds.

	Search Budget Consumed					Overall
Strategy	20%	40%	60%	80%	100%	Rank $\downarrow$	Best % $\uparrow$	Top-2 % $\uparrow$
Fixed-1	3.528	3.389	3.792	4.000	4.208	3.783	15.0	25.0
Fixed-25%	2.583	3.306	3.556	3.264	3.167	3.175	11.7	35.0
Fixed-50%	3.611	3.458	2.958	2.972	2.944	3.189	10.6	27.2
Full	4.639	4.931	4.944	4.778	4.861	4.831	3.3	8.3
AdaEva-S	3.944	3.431	3.097	3.014	2.625	3.222	8.9	27.8

Appendix

Table 5: EoH anytime held-out performance, reported as mean rank (lower is better) at each search-budget checkpoint. Results are macro-averaged over the three CO benchmarks and two LLM backbones (Devstral and Qwen2.5-Coder). Bold and underline denote the best and runner-up strategies, respectively, in each column; higher values are better for the Best % and Top-2 % columns.

	Search Budget Consumed					Overall
Strategy	20%	40%	60%	80%	100%	Rank $\downarrow$	Best % $\uparrow$	Top-2 % $\uparrow$
Fixed-1	4.472	4.639	4.556	4.639	4.681	4.597	6.7	12.8
Fixed-25%	2.889	2.708	2.806	3.014	2.986	2.881	16.7	32.8
Fixed-50%	3.569	3.167	3.403	3.333	3.569	3.408	6.7	18.9
Full	4.889	5.097	5.111	5.000	4.569	4.933	1.7	5.0
AdaEva-S	2.472	2.625	2.625	2.389	2.681	2.558	19.4	47.8

Appendix

Table 6: HiFo-Prompt anytime held-out performance, reported as mean rank (lower is better) at each search-budget checkpoint. Results are macro-averaged over the three CO benchmarks and two LLM backbones (Devstral and Qwen2.5-Coder). Bold and underline denote the best and runner-up strategies, respectively, in each column; higher values are better for the Best % and Top-2 % columns.

	Search Budget Consumed					Overall
Strategy	20%	40%	60%	80%	100%	Rank $\downarrow$	Best % $\uparrow$	Top-2 % $\uparrow$
(1+1)-LLaMEA	3.917	5.000	4.083	4.750	4.833	4.517	0.0	3.3
(10+10)-LLaMEA (Full)	3.917	3.333	3.333	3.250	3.500	3.467	0.0	26.7
Fixed-1	7.000	7.000	7.000	6.833	6.833	6.933	0.0	0.0
Fixed-25%	4.500	3.667	4.500	4.583	4.750	4.400	0.0	13.3
Fixed-50%	5.083	5.000	5.083	4.333	4.583	4.817	3.3	10.0

Appendix

Table 7: LLaMEA anytime held-out performance, reported as mean rank (lower is better) at each search-budget checkpoint. Results are macro-averaged over the 24 BBOB functions and two LLM backbones (Devstral and Qwen2.5-Coder), with the original single-solution

(1+1)

-LLaMEA configuration included as an additional reference. Bold and underline denote the best and runner-up strategies, respectively, in each column; higher values are better for the Best % and Top-2 % columns.

		$C{=}100$ (in-distribution)			$C{=}500$ (capacity-shifted)
Budget	Method	1k	5k	10k	1k	5k	10k
20%	Fixed-1	3.59	1.09	0.56	6.31	1.83	1.23
		[2.59, 4.33]	[0.69, 1.39]	[0.39, 1.01]	[4.82, 7.05]	[1.58, 2.43]	[0.93, 1.46]
	Fixed-25%	3.84	2.23	1.83	4.57	3.13	2.60
		[2.34, 4.58]	[1.78, 2.63]	[1.56, 2.05]	[3.08, 5.56]	[2.68, 3.28]	[2.30, 2.93]
	Fixed-50%	4.83	2.18	1.36	12.02	5.61	4.19

Appendix

Table 8: Anytime held-out performance of EoH on Online Bin Packing using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for in-distribution capacity

C{=}100

and capacity-shifted

C{=}500

across three instance sizes (1k, 5k, and 10k). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. (Part 1)

		$C{=}100$ (in-distribution)			$C{=}500$ (capacity-shifted)
Budget	Method	1k	5k	10k	1k	5k	10k
60%	Fixed-1	4.08	1.19	0.54	12.77	3.97	2.33
		[3.09, 4.83]	[0.94, 1.69]	[0.46, 1.06]	[11.77, 13.51]	[3.67, 4.67]	[2.15, 2.60]
	Fixed-25%	3.34	1.09	0.59	10.53	3.42	2.10
		[2.10, 3.84]	[0.64, 1.49]	[0.41, 0.88]	[9.04, 11.53]	[2.98, 3.87]	[1.78, 2.43]
	Fixed-50%	4.08	1.19	0.86	9.54	3.87	2.18

Appendix

Table 9: Anytime held-out performance of EoH on Online Bin Packing using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for in-distribution capacity

C{=}100

and capacity-shifted

C{=}500

across three instance sizes (1k, 5k, and 10k). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. (Part 2)

		$\leq 100$ (in-distribution)	$101$ – $150$	$>150$ (extrapolation)
Budget	Method	12 instances	11 instances	6 instances
20%	Fixed-1	0.034	0.168	0.694
		[0.008, 0.406]	[0.015, 0.370]	[0.159, 0.906]
	Fixed-25%	0.028	0.318	0.521
		[0.008, 0.403]	[0.015, 0.499]	[0.124, 0.988]
	Fixed-50%	0.244	0.744	0.873

Appendix

Table 10: Anytime held-out performance of EoH on TSPLIB using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for three instance-size groups:

\leq 100

cities (12 in-distribution instances),

101

–

150

cities (11 instances), and

>150

cities (6 extrapolation instances). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. (Part 1)

		$\leq 100$ (in-distribution)	$101$ – $150$	$>150$ (extrapolation)
Budget	Method	12 instances	11 instances	6 instances
60%	Fixed-1	0.059	0.203	0.533
		[0.008, 0.403]	[0.015, 0.381]	[0.132, 0.685]
	Fixed-25%	0.024	0.244	0.486
		[0.004, 0.403]	[0.077, 0.526]	[0.102, 0.705]
	Fixed-50%	0.012	0.085	0.399

Appendix

Table 11: Anytime held-out performance of EoH on TSPLIB using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for three instance-size groups:

\leq 100

cities (12 in-distribution instances),

101

–

150

cities (11 instances), and

>150

cities (6 extrapolation instances). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. (Part 2)

Budget	Method	$m{=}5$ (30)	$m{=}10$ (40)	$m{=}20$ (40)
20%	Fixed-1	0.00	0.51	1.49
		[0.00, 0.07]	[0.29, 0.81]	[1.01, 1.86]
	Fixed-25%	0.00	0.32	1.34
		[0.00, 0.00]	[0.22, 0.57]	[0.73, 1.57]
	Fixed-50%	0.00	0.43	1.39
		[0.00, 0.04]	[0.24, 0.66]	[0.80, 1.62]

Appendix

Table 12: Anytime held-out performance of EoH on the Taillard flow-shop scheduling benchmark using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for three problem-size groups:

m{=}5

(30 instances),

m{=}10

(40 instances), and

m{=}20

(40 instances). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. (Part 1)

Budget	Method	$m{=}5$ (30)	$m{=}10$ (40)	$m{=}20$ (40)
100%	Fixed-1	0.00	0.43	1.43
		[0.00, 0.07]	[0.25, 0.66]	[0.84, 1.83]
	Fixed-25%	0.00	0.41	1.22
		[0.00, 0.00]	[0.18, 0.58]	[0.71, 1.56]
	Fixed-50%	0.00	0.32	1.23
		[0.00, 0.00]	[0.21, 0.61]	[0.57, 1.60]

Appendix

Table 13: Anytime held-out performance of EoH on the Taillard flow-shop scheduling benchmark using the Devstral backbone. Cells report the incumbent’s objective value at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), for three problem-size groups:

m{=}5

(30 instances),

m{=}10

(40 instances), and

m{=}20

(40 instances). Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Lower values are better. The lower block reports the percentage of instances whose normalized gap is at or below the upper bound (gap

\leq 0

). (Part 2)

Budget	Method	$d{=}5$	$d{=}10$	$d{=}20$	All
20%	(1+1)-LLaMEA	0.505	0.458	0.429	0.464
		[0.477, 0.517]	[0.435, 0.463]	[0.401, 0.435]	[0.438, 0.472]
	(10+10)-LLaMEA	0.526	0.471	0.428	0.474
		[0.517, 0.543]	[0.452, 0.474]	[0.389, 0.431]	[0.462, 0.475]
	Fixed-1	0.486	0.433	0.402	0.439
		[0.458, 0.521]	[0.424, 0.459]	[0.400, 0.418]	[0.428, 0.464]

Appendix

Table 14: Anytime held-out performance of LLaMEA on BBOB using the Devstral backbone. Cells report AOCC at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), evaluated on held-out instances across all 24 BBOB functions and dimensions

5

10

, and

20

. All denotes the mean across the three dimensions. Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Higher AOCC is better. (Part 1)

Budget	Method	$d{=}5$	$d{=}10$	$d{=}20$	All
60%	(1+1)-LLaMEA	0.513	0.463	0.430	0.469
		[0.476, 0.530]	[0.436, 0.470]	[0.400, 0.438]	[0.437, 0.480]
	(10+10)-LLaMEA	0.552	0.494	0.433	0.488
		[0.539, 0.557]	[0.476, 0.496]	[0.408, 0.437]	[0.483, 0.501]
	Fixed-1	0.472	0.426	0.395	0.430
		[0.441, 0.513]	[0.409, 0.460]	[0.391, 0.417]	[0.414, 0.463]

Appendix

Table 15: Anytime held-out performance of LLaMEA on BBOB using the Devstral backbone. Cells report AOCC at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), evaluated on held-out instances across all 24 BBOB functions and dimensions

5

10

, and

20

. All denotes the mean across the three dimensions. Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Higher AOCC is better. (Part 2)

Budget	Method	$d{=}5$	$d{=}10$	$d{=}20$	All
20%	Fixed-1	0.499	0.425	0.365	0.435
		[0.000, 0.515]	[0.000, 0.432]	[0.000, 0.385]	[0.000, 0.439]
	Fixed-25%	0.545	0.474	0.431	0.477
		[0.526, 0.568]	[0.453, 0.488]	[0.377, 0.434]	[0.473, 0.497]
	Fixed-50%	0.544	0.451	0.404	0.473
		[0.537, 0.624]	[0.451, 0.460]	[0.389, 0.415]	[0.462, 0.483]

Appendix

Table 16: Anytime held-out performance of LLaMEA on BBOB using the Qwen2.5-Coder backbone. Cells report AOCC at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), evaluated on held-out instances across all 24 BBOB functions and dimensions

5

10

, and

20

. All denotes the mean across the three dimensions. Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Higher AOCC is better. (Part 1)

Budget	Method	$d{=}5$	$d{=}10$	$d{=}20$	All
60%	Fixed-1	0.499	0.425	0.365	0.435
		[0.000, 0.515]	[0.000, 0.432]	[0.000, 0.385]	[0.000, 0.439]
	Fixed-25%	0.560	0.456	0.445	0.476
		[0.433, 0.561]	[0.341, 0.496]	[0.428, 0.455]	[0.400, 0.504]
	Fixed-50%	0.557	0.465	0.409	0.487
		[0.419, 0.596]	[0.453, 0.485]	[0.376, 0.437]	[0.411, 0.493]

Appendix

Table 17: Anytime held-out performance of LLaMEA on BBOB using the Qwen2.5-Coder backbone. Cells report AOCC at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), evaluated on held-out instances across all 24 BBOB functions and dimensions

5

10

, and

20

. All denotes the mean across the three dimensions. Main entries are medians across five search seeds, with interquartile ranges shown in brackets. Higher AOCC is better. (Part 2)

		$\leq 100$ (in-distribution)	$101$ – $150$	$>150$ (extrapolation)
Budget	Method	12 instances	11 instances	6 instances
20%	Fixed-1	0.024	0.287	0.493
		[0.004, 0.403]	[0.015, 0.404]	[0.107, 0.694]
	Fixed-25%	0.020	0.037	0.338
		[0.004, 0.403]	[0.002, 0.174]	[0.057, 0.635]
	Fixed-50%	0.172	0.372	0.597

Appendix

Table 18: Anytime held-out performance of EoH on TSPLIB using the Gemini-3.1-Flash-Lite backbone. Cells report the median optimality gap across five search seeds at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), with the interquartile range shown beneath. Results are grouped by instance size:

\leq 100

(12 instances),

101

–

150

(11 instances), and

>150

(6 extrapolation instances). Lower values are better, and the best median within each budget block and instance-size group is shown in bold . (Part 1)

		$\leq 100$ (in-distribution)	$101$ – $150$	$>150$ (extrapolation)
Budget	Method	12 instances	11 instances	6 instances
60%	Fixed-1	0.052	0.247	0.400
		[0.008, 0.403]	[0.015, 0.338]	[0.101, 0.655]
	Fixed-25%	0.024	0.028	0.333
		[0.004, 0.403]	[0.002, 0.236]	[0.060, 0.542]
	Fixed-50%	0.103	0.107	0.623

Appendix

Table 19: Anytime held-out performance of EoH on TSPLIB using the Gemini-3.1-Flash-Lite backbone. Cells report the median optimality gap across five search seeds at five search-budget checkpoints (

20\%

40\%

60\%

80\%

, and

100\%

), with the interquartile range shown beneath. Results are grouped by instance size:

\leq 100

(12 instances),

101

–

150

(11 instances), and

>150

(6 extrapolation instances). Lower values are better, and the best median within each budget block and instance-size group is shown in bold . (Part 2)

Parameter	Value	EoH/OBP	EoH/TSP	EoH/FSSP	LLaMEA/BBOB
$\underline{n}_{0}$ (AdaEva-R)	1	$-0.22$	$+0.02$	$0.00$	$+21.37$
	5	$0^{\dagger}$	$0^{\dagger}$	$0^{\dagger}$	$0^{\dagger}$
	10	$+0.84$	$+0.05$	$-0.03$	$+32.29$
$\Delta$	1	$0^{\dagger}$	$0^{\dagger}$	$0^{\dagger}$	$0^{\dagger}$
$\Delta$	3	$+1.05$	$+0.02$	$-0.01$	$+28.92$
$\underline{n}_{0}$ (AdaEva-S)	1	$-0.03$	$-0.08$	$+0.02$	$+2.83$

Appendix

Table 20: Configuration sensitivity of AdaEva. Each cell is the percent change in the incumbent’s full-pool quality under the ablated configuration relative to the default within the same seed, averaged across budget checkpoints (

\Delta(c)

); positive values indicate worse performance than the default. One factor is varied at a time, so each configuration differs from its default in exactly one parameter.

\underline{n}_{0}

is varied separately for AdaEva-S and AdaEva-R, while

\eta

is specific to AdaEva-S and the racing parameters are specific to AdaEva-R. The prompt rows correspond to the LLaMEA feedback ablation. Defaults are marked

\dagger

, and – denotes a configuration with no usable run.

Large Language Model-Guided Optimization

Large Language Model-Guided Optimization uses language models to propose, steer, and refine solutions to hard search and design problems.

See all 601 papers

AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization
Feb 23, 2026Mert Cemri, Shubham Agrawal, Akshat Gupta +9Large Language Model-Guided Optimization LLMs for Evolutionary Optimization
University of California, Berkeley · Bespoke Labs
Online Automated Algorithm Design with Large Language Models
Sep 21, 2026Zhiyao Zhang, Yichen Li, Xingyu Wu +2Automated Algorithm Discovery Black-Box Optimization
Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University, Hong Kong SAR 999077, China · College of Computer Science, Chongqing University, Chongqing 400044, China
PACE: Primitive-Aware Code Evolution for Automated Algorithm Design
Aug 7, 2026Zhuoliang Xie, Ruihao Zheng, Xiang Xu +2Automated Algorithm Discovery Automated Heuristic Design
1Southern University of Science and Technology · 2Shenzhen University

Feb 23, 2026J/K move · Enter open · S save

AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization

Mert Cemri, Shubham Agrawal, Akshat Gupta, Shu Liu+8

University of California, Berkeley · Bespoke Labs

Large Language Model-Guided Optimization LLMs for Evolutionary Optimization Evolutionary Optimization LLM-Based Program Synthesis Zeroth-Order Optimization

Open paper AskPDF arXiv

The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operators within evolutionary loops. While effective, these systems are currently governed by static schedules that fail to account for the non-stationary dynamics of the search process. This rigidity results in substantial computational waste, as resources are indiscriminately allocated to stagnating populations while promising frontiers remain under-exploited. We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem. AdaEvolve uses an "accumulated improvement signal" to unify decisions across three levels: Local Adaptation, which dynamically modulates the exploration intensity within a population of solution candidates; Global Adaptation, which routes the global resource budget via bandit-based scheduling across different solution candidate populations; and Meta-Guidance which generates novel solution tactics based on the previously generated solutions and their corresponding improvements when the progress stalls. We demonstrate that AdaEvolve consistently outperforms the open-sourced baselines across 185 different open-ended optimization problems including combinatorial, systems optimization and algorithm design problems.

Abstract

Large Language Model-Guided Optimization

Explore similar work

AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization

Online Automated Algorithm Design with Large Language Models

PACE: Primitive-Aware Code Evolution for Automated Algorithm Design