Hyper-heuristics have emerged as a way to raise the level of generality of search techniques
for computational search problems. This is in contrast to many approaches, which represent …

Many real-world optimization problems have multiple interacting components. Each of these
can be an NP-hard problem, and they can be in conflict with each other, ie, the optimal …

Real-world problems often comprise multiple interdependent sub-problems. The
interdependency between sub-problems makes the original problem complex. The …

Over the past 30 years, many researchers in the field of evolutionary computation have put a
lot of effort to introduce various approaches for solving hard problems. Most of these …

Metaheuristics algorithms are designed to find approximate solutions for challenging
optimization problems. The success of the algorithm over a given optimization task relies on …

The paper introduces a novel many-objective evolutionary method, with a diversity-based
selection operator and aims to fill the “gaps” in the Pareto Front approximation and to …

This research proposes a novel indicator-based hybrid evolutionary approach that combines
approximate and exact algorithms. We apply it to a new bi-criteria formulation of the …

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP
comprises an entanglement of a travelling salesman problem (TSP) and a knapsack …

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 …