Combining the filtered evaluation function with coevolutionary shared niching in genetic algorithms

In this paper, we propose the combination of filtered evaluation and coevolutionary shared niching (CSN) for extending the search ability of genetic algorithms (GA). The proposed scheme can overcome the problems of the filtering GA (FGA) and the CSN. The successful optimization ability of the FGA is supported by the filtered evaluation method that can modify the landscape for escaping local optima. However, the problem of the FGA is the relatively high cost to maintain the filter. The CSN can autonomously maintain the shared distance using the coevolution between two populations (called customers and businessmen). However, the escaping ability from local optima of the CSN is still insufficient. Therefore, the combination of the filtered evaluation and the CSN is proposed, to reduce the cost of the FGA filter. The effectiveness of the proposed scheme is confirmed through test problems.