Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been
applied in a wide range of applications. However, they still suffer from a high computational …

Traditional evolution algorithms tend to start the search from scratch. However, real-world
problems seldom exist in isolation and humans effectively manage and execute multiple …

The evolutionary algorithm (EA) is a nature-inspired population-based search method that
works on Darwinian principles of natural selection. Due to its strong search capability and …

The concurrence of the oil demand increment and running out of fossil fuels have brought
about special attention toward the tight and low permeability reservoirs. The decision …

The uncertain capacitated arc routing problem has many real-world applications in logistics
domains. Genetic programming (GP) is a promising approach to training routing policies to …

Multitask genetic programming methods have been applied to various domains, such as
classification, regression, and combinatorial optimization problems. Most existing multitask …

Computational biology has changed how healthcare systems and biomedical engineering
work. Nature-inspired intelligent computing (NIIC) approaches in predicting potential …

In the design evolution of a product, designers often require solving similar functions
repeatedly across different designs. These functions are usually related to each other and …

Searching for symbolic models plays an important role in a wide range of domains such as
neural architecture search and automatic program synthesis. Genetic programming is a …

We studied two problems called the Traveling Repairman Problem (TRPTW) and Traveling
Salesman Problem (TSPTW) with time windows. The TRPTW wants to minimize the sum of …