An efficient genetic algorithm with less fitness evaluation by clustering

To solve a general problem with genetic algorithms, 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 a large population. To solve this problem, we propose a hybrid GA based on clustering, which considerably reduces the evaluation number without any loss of performance. The algorithm divides the whole population into several clusters, and evaluates only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly, which can maintain a large population with less number of evaluations. Several benchmark tests have been conducted and the results show that the proposed GA is very efficient