Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization: Including Real-World Medical Applications

In recent years, the use of Artificial Intelligence (AI) has become prevalent in a large number of societally relevant, real-world problems, e.g., in the domains of engineering and health care. The field of Evolutionary Computation (EC) can be considered to be a sub-field of AI, concerning optimization using Evolutionary Algorithms (EAs), which...

Hybridizing Hypervolume based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation

Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation

Evolutionary algorithms (EAs) are well-known to be well suited for multi-objective (MO) optimization. However, especially in the case of real-valued variables, classic domination-based approaches are known to lose selection pressure when approaching the Pareto set. Indicator-based approaches, such as optimizing the uncrowded hypervolume (UHV)...

Fitness-based Linkage Learning in the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm

The recently introduced real-valued gene-pool optimal mixing evolutionary algorthm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem, which often is unknown a...