Process / pipelineSimulation / optimization
Multi-Objective Genetic Algorithm (MOGA) — Evolutionary Search for Pareto-Optimal Solutions
A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-dominated solutions for the decision-maker to choose among.
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Sources
- Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673
- 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 ↗
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Agent-based genetic algorithmAgent-based multi-objective optimizationAgent-based NSGA-IIBayesian NSGA-IIDeterministic Genetic AlgorithmMulti-objective agent-based modelingMulti-objective ant colony optimizationMulti-objective cellular automataMulti-objective dynamic programmingMulti-objective particle swarm optimizationMulti-objective simulated annealingMulti-objective Tabu SearchPolicy Scenario Genetic AlgorithmPolicy Scenario Multi-Objective OptimizationRobust Genetic AlgorithmRobust NSGA-IIStochastic NSGA-II