MTAga
MTAga, short for Multi-Target Adaptive Genetic Algorithm, refers to a family of evolutionary algorithms designed to optimize multiple objectives simultaneously. Building on classical genetic algorithms, MTAga integrates adaptive control of genetic operators, Pareto-based selection, and diversity-preserving mechanisms to approximate a Pareto front of high-quality trade-offs.
Key features include adaptive mutation and crossover rates that respond to convergence signals, a potential dynamic
Applications span engineering design, energy systems planning, logistics and scheduling, and machine learning hyperparameter tuning. MTAga
Relationship and variants: concepts similar to MTAga appear under different names in the literature; some implementations
Note: This article describes the concept in neutral, concise terms. MTAga may refer to different specific methods