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genetic algorithms

topic3 events
papersTODAY 04:00 UTC

arXiv paper applies surrogate-assisted evolutionary algorithms to agent-based model calibration

A new arXiv preprint addresses the difficulty of calibrating agent-based models, whose objective landscapes are stochastic, rugged, and expensive to evaluate through simulation. The authors adapt inner-loop surrogate-assisted evolutionary computation, combining genetic algorithms and particle swarm optimization to reduce the number of costly black-box evaluations. The work sits at the intersection of machine learning and simulation-based modeling.

research/agent-based-models/optimization/arxiv
papersTODAY 04:00 UTC

HypoEvolve Applies Genetic Algorithms to Multi-Agent LLM Hypothesis Discovery

A new arXiv paper introduces HypoEvolve, a system that combines multi-agent large language models with evolutionary search to generate scientific hypotheses. The approach uses critique, comparison and revision cycles to refine candidate explanations, though the abstract notes limitations in current agent-based discovery systems. It sits within a broader trend of pairing LLM agents with evolutionary optimization for research tasks.

agents/multi-agent/research/genetic-algorithms
papersSEP 10 04:00 UTC

Genetic Algorithm Approach to Bayesian Network Fusion Under Treewidth Limits

A new arXiv paper proposes using evolutionary computation to merge several Bayesian networks into a single consensus structure. The method treats fusion as an optimization problem, seeking a combined network that preserves key dependencies from each input while keeping treewidth bounded for computational tractability.

bayesian-networks/genetic-algorithms/probabilistic-models/structure-learning