The factors determining a drug's success are manifold, making de novo drug design an
inherently multi-objective optimisation (MOO) problem. With the advent of machine learning …

Multimodal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions
in a single simulation run has practical relevance to problem solving across many fields …

Various real-world applications can be classified as expensive multimodal optimization
problems. When surrogate-assisted evolutionary algorithms (SAEAs) are employed to tackle …

Many real-world applications can be formulated as expensive multimodal optimization
problems (EMMOPs). When surrogate-assisted evolutionary algorithms (SAEAs) are …

Drug discovery is a challenging multi-objective problem where numerous pharmaceutically
important objectives need to be adequately satisfied for a solution to be found. The problem …

Multimodal multiobjective optimization problems (MMOPs) are commonly seen in real-world
applications. Many evolutionary algorithms have been proposed to solve continuous …

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal
solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective …

Finding personalized biomarkers for disease prediction of patients with cancer remains a
massive challenge in precision medicine. Most methods focus on one subnetwork or module …

Considering that cancer is resulting from the comutation of several essential genes of
individual patients, researchers have begun to focus on identifying personalized edge …

For many combinatorial problems, finding a single solution is not enough. This is clearly the
case for multi-objective optimization problems, as they have no single “best solution” and …