Partially evaluated genetic algorithm based on fuzzy c-means algorithm

To find the optimal solution with genetic algorithm, it is desirable to maintain the population size as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a partially evaluated GA based on fuzzy clustering, which considerably reduces evaluation cost without any loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness according to membership matrix. The results with nine benchmark functions are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients for measuring the similarity between the representative and its members in fitness distribution.