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Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.

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Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.

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Commentaires : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.

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Accessoires : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.
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Commentaires : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.
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Abstract:Expensive optimization issues (EOPs)
are black-field tasks with costly objective evaluations and
no gradient access, making the evaluation finances the important thing bottleneck.
Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via
surrogate predictions, but standard surrogates typically require
frequent retraining as populations evolve, incurring overhead.
This paper proposes R2SAEA, a reinforcement-educated relation-based large language
model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process.
To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for
offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5
with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong
SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining.
Code and models are available at this https URL.
descr_nok, , , sh_XXX
État : Wanneroo
Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.
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Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.
Pub, livres : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back eva
Commentaires : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.
Ancien texte : Abstract:Expensive optimization issues (EOPs) are black-field tasks with costly objective evaluations and no gradient access, making the evaluation finances the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations via surrogate predictions, but standard surrogates typically require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-educated relation-based large language model (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning process. To allow environment friendly inference in evolutionary loops, we develop an anchor-primarily based iterative context development technique that reduces immediate complexity from quadratic to linear in inhabitants size, and a voting-primarily based aggregation scheme that converts predicted relations into scores for offspring choice. We additional build an RL pipeline from evolutionary trajectories and nice-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-artwork optimization performance over strong SAEA baselines and general LLMs. Quantization additionally allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm with out per-generation retraining. Code and models are available at this https URL.

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