Heuristic operators, redundant mapping and other issues in genetic algorithms

This paper uses the 0-1 knapsack problems (KPs) to investigate such issues as early convergence, exploration versus exploitation, redundant mapping and the role of heuristic operators etc. in genetic algorithms (GAs) with the (/spl mu/+/spl lambda/)-strategy. We use the order-based representation for chromosome and propose two different decoding approaches, order-decoding (preserving redundancy) and cycle-decoding (eliminating redundancy), to decode it. A new crossover and two new mutation operators are also proposed. KPs with various kinds of item numbers, capacities, and correlations between profits and weights are tested with a wide range of possible combinations of genetic operators. Computer simulation results show that heuristic operators must be used appropriately to achieve better results; exploration operators must be used with care; super individuals, early convergence and redundant mapping are not harmful for GAs.