Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmo Genético Multiobjetivo (MOGA)× | Algoritmo Genético× | |
|---|---|---|
| Campo≠ | Simulación | Optimización |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1984 | 1975 |
| Autor original≠ | Schaffer, J. D. (early MOGA); Goldberg, D. E. (GA foundations) | John Henry Holland |
| Tipo≠ | Population-based evolutionary optimizer | Population-based metaheuristic |
| Fuente seminal≠ | Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley. ISBN: 9780201157673 | Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. link ↗ |
| Alias≠ | MOGA, Multi-objective GA, Evolutionary multi-objective optimization, EMO | GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail. |
| ScholarGateConjunto de datos ↗ |
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