A comparison of GAs using penalizing infeasible solutions and repairing infeasible solutions on average capacity knapsack

Authors:
Jun He;Yuren Zhou
Affiliations:
CERCIA, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, UK;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
Venue:
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Year:
2007

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Abstract

Different constraint handling techniques have been incorporated with genetic algorithms (GAs), however most of current studies are based on computer experiments. The paper makes an theoretical analysis of GAs using penalizing infeasible solutions and repairing infeasible solutions on average knapsack problem. It is shown that GAs using the repair method is more efficient than GAs using the penalty method on average capacity knapsack problems.