In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices...In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.展开更多
文摘In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.
