QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.
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TRUST-TAEA is a trustworthiness-guided two-archive evolutionary algorithm using variable-grouping sparse search that outperforms or matches existing methods on large-scale multi-objective benchmarks and a microgrid dispatch problem.
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Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
QD-LLM evolves prompt embeddings via neuroevolution in a quality-diversity framework, delivering 46% higher coverage and 41% higher QD-score than prior methods on coding and writing benchmarks.
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TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization
TRUST-TAEA is a trustworthiness-guided two-archive evolutionary algorithm using variable-grouping sparse search that outperforms or matches existing methods on large-scale multi-objective benchmarks and a microgrid dispatch problem.