A Coevolutionary Algorithm Based on Dimension Identifying
YANG Li-Ping1,2, HUANG Hou-Kuan1, YANG Xiao-Hong2
1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 1000442. School of Computer and Information Engineering, Shandong University of Finance, Jinan 250014
Abstract：How to integrate the dimensional information of the problem domain into coevolution is studied. Through the analysis of the outcome characteristics of interactions between individuals, a strict dimension identifying method is proposed. Thus, an efficient and reliable coevolutionary algorithm is designed. It can automatically identify dimensions of the problem by the outcome characteristics between individuals with only the current highest test in each dimension maintained and monotonic progress on all dimensions sustained. In this algorithm, the archive can achieve minimum size to guarantee its practicability. Experimental comparisons demonstrate that the algorithm performs more efficiently than others.
杨莉萍，黄厚宽，杨晓红. 一种基于维度识别的协同进化算法[J]. 模式识别与人工智能, 2008, 21(4): 453-461.
YANG Li-Ping, HUANG Hou-Kuan, YANG Xiao-Hong. A Coevolutionary Algorithm Based on Dimension Identifying. , 2008, 21(4): 453-461.
[1] de Jong E D. Towards a Bounded Pareto-Coevolution Archive // Proc of the Congress on Evolutionary Computation. Portland, USA, 2004, Ⅱ: 2341-2348 [2] Hillis D W. Co-Evolving Parasites Improve Simulated Evolution as an Optimization Procedure. Physica D, 1990, 42(1/2/3): 228-234 [3] Paredis J. Coevolutionary Computation. Artificial Life, 1995, 2(4): 355-375 [4] Rosin C D, Belew R K. New Methods for Competitive Coevolution. Evolutionary Computation, 1997, 5(1): 1-29 [5] Ficici S G, Pollack J B. Pareto Optimality in Coevolutionary Learning // Proc of the 6th European Conference on Advances in Artificial Life. Prague, Czech Republic, 2001: 316-325 [6] de Jong E D, Pollack J B. Ideal Evaluation from Coevolution. Evolutionary Computation, 2004,12(2): 159-192 [7] Watson R A, Pollack J B. Coevolutionary Dynamics in a Minimal Substrate // Proc of the Genetic and Evolutionary Computation Conference. San Francisco, USA, 2001: 702-709 [8] de Jong E D. A Monotonic Archive for Pareto-Coevolution. Evolutionary Computation, 2007, 15(1): 61-93 [9] Bucci A, Pollack J B. A Mathematical Framework for the Study of Coevolution // Proc of the Foundations of Genetic Algorithms Workshop. San Francisco, USA, 2003: 221-235 [10]Bucci A, Pollack J B. Focusing Versus Intransitivity: Geometrical Aspects of Coevolution // Proc of the Genetic and Evolutionary Computation Conference. Chicago, USA, 2003: 250-261 [11] Bucci A, Pollack J B, de Jong E D. Automated Extraction of Problem Structure // Proc of the Genetic and Evolutionary Conputation Conference. Seattle, USA, 2004: 501-512 [12] de Jong E D, Bucci A. DECA: Dimension Extracting Coevolutionary Algorithm // Proc of the Genetic and Evolutionary Computation Conference. Seattle, USA, 2006: 313-320
2008, Vol. 21 
