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Agent-based NSGA-II/Evidence
Method evidence record

Agent-based NSGA-II

Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems whose performance emerges from the micro-level interactions of autonomous agents rather than from analytically tractable equations.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Agent-Based Non-dominated Sorting Genetic Algorithm II — Simulation-Driven Evolutionary Multi-Objective Optimization
Taxonomic method record · process-pipeline / simulation
  • Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. · DOI 10.1109/4235.996017
  • Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162. · DOI 10.1057/jos.2010.3
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Curated claims

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyAgent-Based Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketAgent-based multi-objective optimizationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMulti-objective genetic algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketStochastic NSGA-IImachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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