An Adaptive Fitness Function for Evolutionary Algorithms Using Heuristics and Prediction
Ping Tang, Gordon K. Lee · 2006
A genetic algorithm usually performs a search over a complex and multimodal space and is an important component in several applications such as evolutionary learning and optimization. The search is dependent on several parameters including the fitness function, parent selection process, mutation rate and crossover rate. The fitness function is an important component in the evolutionary process since this performance metric is used to select the best individuals in a population that will then evolve through the mutation, crossover and reproduction process in successive generations. In this paper, a fitness function is developed that employs heuristic information based upon past history, current information and future knowledge; in particular, prediction and expectation are integrated into the fitness function. Simulation results show an improvement over classical fitness techniques.