A relation-based genetic algorithm for partitioning problems with applications

Authors:
Jiah-Shing Chen;Yao-Tang Lin;Liang-Yu Chen
Affiliations:
Department of Information Management, National Central University, Jhongli, Taiwan;Department of Information Management, National Central University, Jhongli, Taiwan;Department of Information Management, National Central University, Jhongli, Taiwan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

This paper proposes a new relation-based genetic algorithm named relational genetic algorithm (RGA) for solving partitioning problems. In our RGA, a relation-oriented representation (or relational encoding) is adopted and corresponding genetic operators are redesigned. The relational encoding is represented by the equivalence relation matrix which has a 1-1 and onto correspondence with the class of all possible partitions. It eliminates the redundancy of previous GA representations and improves the performance of genetic search. The generalized problem-independent operators we redesigned manipulate the genes without requiring specific heuristics in the process of evolution. In addition, our RGA also supports a variable number of subsets. It works without requiring a fixed number of subsets in advance. Experiments for solving some well-known classic partitioning problems by RGA and GGA with and without heuristics are performed. Experimental results show that our RGA is significantly better than GGA in all cases with larger problem sizes.