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Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization

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arXiv:2609.13247v1 Announce Type: new Abstract: Calibrating an agent-based model (ABM) is difficult because its objective landscape is stochastic and rugged, and can be evaluated only through costly black-box simulations.

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Original headline: Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization

Read the original: arXiv — Multiagent Systems (cs.MA)

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Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization

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A unified vertical alignment and earthwork model in road design with a new convex optimization model for road networks

Роман Венедиктов («Эмерджентные мультиагентные системы»): «Сегодня сам факт использования ИИ стал обычной частью корпоративной ИТ-среды»

A minimal model of money creation under regulatory constraints