Fitness landscapes were proposed in 1932 as an abstract notion for understanding
biological evolution and were later used to explain evolutionary algorithm behaviour. The …

Boolean functions have numerous applications in domains as diverse as coding theory,
cryptography, and telecommunications. Heuristics play an important role in the construction …

AutoML tackles the problem of automatically configuring machine learning pipelines to
specific data analysis problems. These pipelines may include methods for preprocessing …

Real-world problems often consist of several interdependent subproblems. The degree of
interaction of the subproblems is associated with the complexity of the problem and solving …

Unlike other NP-hard problems, the constraints on the traveling tournament problem are so
pressing that it's hardly possible to randomly generate a valid solution, for example, to use in …

In this paper, we propose a method to solve a bi-objective variant of the well-studied
traveling thief problem (TTP). The TTP is a multi-component problem that combines two …

For solving combinatorial optimisation problems with metaheuristics, different search
operators are applied for sampling new solutions in the neighbourhood of a given solution. It …

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many
problems. When using GAs for evolving solutions, often fitness evaluation is the most …

Abstract The Thief Orienteering Problem (ThOP) is a multi-component problem that
combines features of two classic combinatorial optimization problems: Orienteering Problem …

Genetic programming is an often-used technique for symbolic regression: finding symbolic
expressions that match data from an unknown function. To make the symbolic regression …