Scaling Up Evolutionary Programming Algorithms

Most analytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EP (CEP) [1, 2], fast EP (FEP).

[1]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[2]  Cornelia Kappler,et al.  Are Evolutionary Algorithms Improved by Large Mutations? , 1996, PPSN.

[3]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[4]  Thomas Bäck,et al.  An Overview of Evolutionary Computation , 1993, ECML.

[5]  David B. Fogel,et al.  Evolving artificial intelligence , 1992 .

[6]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[7]  Xin Yao,et al.  Fast Evolutionary Programming , 1996, Evolutionary Programming.

[8]  Xin Yao,et al.  An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes , 1997, Evolutionary Programming.

[9]  Thomas Bäck,et al.  Evolutionary computation: an overview , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[10]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[11]  Hans-Paul Schwefel,et al.  Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

[12]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[13]  David B. Fogel,et al.  Multi-operator Evolutionary Programming: A Preliminary Study on Function Optimization , 1997, Evolutionary Programming.

[14]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.