  The 0-1 Multidimensional Knapsack Problem (MKP) is a classical NP-hard
combinatorial optimization problem with many engineering applications. In this
paper, we propose a novel algorithm combining evolutionary computation with
exact algorithm to solve the 0-1 MKP. It maintains a set of solutions and
utilizes the information from the population to extract good partial
assignments. To find high-quality solutions, an exact algorithm is applied to
explore the promising search space specified by the good partial assignments.
The new solutions are used to update the population. Thus, the good partial
assignments evolve towards a better direction with the improvement of the
population. Extensive experimentation with commonly used benchmark sets shows
that our algorithm outperforms the state of the art heuristic algorithms, TPTEA
and DQPSO. It finds better solutions than the existing algorithms and provides
new lower bounds for 8 large and hard instances.

Artificial Intelligence