A Novel Fitness Evaluation Method for Evolutionary Algorithms
Fitness evaluation is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. But these algorithms may require huge computation power for solving nonlinear programming problems. This paper proposes a novel fitness evaluation approach which employs similarity-base learning embedded in a classical differential evolution (SDE) to evaluate all new individuals. Each individual consists of three elements: parameter vector (v), a fitness value (f), and a reliability value(r). The f is calculated using NFEA, and only when the r is below a threshold is the f calculated using true fitness function. Moreover, applying error compensation system to the proposed algorithm further enhances the performance of the algorithm to make r much closer to true fitness value for each new child. Simulation results over a comprehensive set of benchmark functions show that the convergence rate of the proposed algorithm is much faster than much that of the compared algorithms.
evolutionary algorithms differential evolution fitness evaluation similarity-based learning nonlinear programming problems
Wang Ji-feng Tang Ke-zong
Department of Electrical Engineering Huaian College of information technology Huaian, China School of Computer Science and Technology Nanjing University of Science and Technology,Nanjing, Chin
国际会议
成都
英文
600-604
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）