Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is
to determine the best approach for solving a problem at its current state. This way, every time …

The knapsack problem is one of the combinational optimization issues. This problem is an
NP-hard problem. Soft computing methods, including the use of metaheuristic algorithms …

Recent years have witnessed a growing interest in automatic learning mechanisms and
applications. The concept of hyper-heuristics, algorithms that either select among existing …

In this paper, the bi-objective quadratic multiple knapsack problem is tackled with a hybrid
population-based method. The proposed method starts by computing two reference …

The evolutionary algorithm has been extensively used to solve a range of combinatorial
optimization problems. The adaptability of evolutionary algorithm mechanisms provides …

Suppose a manager has to assign agents for multiple projects, where each agent has its
own budget. The manager knows the salary and productivity of each agent, both individually …

In this paper, the multiple‐choice knapsack problem with setups is tackled with an iterative
method, where both local branching and descent method cooperate. First, an iterative …

Hyper-heuristics are potent techniques that represent the synergy of low-level heuristics
when solving optimization problems. This synergy usually leads to better solutions. Similarly …

It is common to find that training of selection hyper-heuristics is done perturbatively. The
process usually starts with a random selection module and iterates over a set of instances …

Although the number of solutions in combinatorial optimization problems (COPs) is finite,
some problems grow exponentially and render exact approaches unfeasible. So …