A mixed strategy for Evolutionary Programming based on local fitness landscape

The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the initial scale of the whole population can be very large. This may lead to the conventional EP taking too long to reach convergence. To combat this problem, EP has been modified in various ways. In particular, modifications of the mutation operator may significantly improve the performance of EP. However, operators are only efficient within certain fitness landscapes. The mixed strategies have therefore been proposed in order to combine the advantages of different operators. The design of a mixed strategy is currently based on the performance of applying individual operators. Little is directly relevant to the information of local fitness landscapes. This paper presents a modified mixed strategy, which automatically adapts to local fitness landscapes, and implements a trai...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Liang Shen, Jun He
• Keywords: Artificial Intelligence, CEC 2010, Fitness Landscape, Mixed Strategy, Mutation Operator