A fast evolutionary algorithm with searching preference

Evolutionary algorithm (EA) has been successfully applied to many numerical optimisation problems in recent years. However, EA has rather slow convergence rates on some optimisation problems. In this paper, a fast evolutionary algorithm (FEA) with searching preference is proposed. Our basic idea is that the better an individual is, the more resources are invested to search the region close to the individual. Two techniques are applied to achieve it: making the best individual found so far always take part in crossover and mutation, and proposing a novel crossover and mutation operator based on simulated annealing. Obviously, the search process emphasises the region around the best individual. Furthermore, we can prove that FEA converges to a global optimum in probability. Numerical simulations are conducted for 19 standard test functions. The performance of FEA is compared with three EAs FEP, OGA/Q and LEA that have been published recently. The results indicate that FEA is effective and efficient. Furthermore, the result obtained by FEP is better than the best result found so far for f(8).