Transfer Evolutionary Optimization

Transfer evolutionary optimization (TrEO) aims to improve the efficiency and effectiveness of optimization algorithms by leveraging knowledge gained from solving related problems. Current research focuses on developing robust benchmark suites for evaluating TrEO algorithms, exploring novel architectures like "learngenes" for efficient knowledge transfer, and adapting TrEO to multi-objective and dynamic optimization problems, including the use of inverse transfer models and surrogate-assisted methods. These advancements are significant because they promise to accelerate the solution of complex real-world problems across diverse domains, from engineering design to machine learning, by reducing computational costs and improving generalization.