Some experiences on solving multiconstraint zero-one knapsack problems with genetic algorithms

In the multiconstraint zero-one knapsack problem one has to decide on how to make efficient use of an entity which consumes multiple resources. The problem is known to be NP-hard, so heuristic solution procedures come into consideration. The purpose of this paper is to analyse different operators within genetic algorithms for solving the multiconstraint zero-one knapsack problem and to compare the results with those of recent investigations of other modern heuristic concepts presented in the literature. Numerical experiences emphasize that one has to incorporate local search improvement operators into simple genetic algorithms to obtain competitive results.