Boosting Evolutionary Optimization via FuzzyClassificationAssisted Selection Abstract   In evolutionary optimization, solution selection is an important operator since it will be normally used to decide the optimization direction via determining new solutions. Most selection methods are objective fitnessbased approaches which will lead to a waste of fitness evaluations. This is because some evaluated but unpromising solutions are discarded without contributing useful search information. We are, thus, motivated to treat the solution selection as a classification procedure, where the selected solutions and discarded solutions belong to different classes. However, another problem is that the difference between xe2x80x98promisingxe2x80x99 and xe2x80x98unpromisingxe2x80x99 solutions becomes fuzzy when iterations go on. Therefore, we employ fuzzy classification to predict the categories of solutions by the fuzzy membership function. And then the predicted results are used to assist solution selection to reduce the number of fitness evaluations. Finally, we propose a fuzzyclassificationassisted selection (FCAS) strategy to boost evolutionary optimization. FCAS is experimentally integrated into two stateoftheart algorithms and studied on three test suites. The results reveal the efficiency of FCAS for boosting evolutionary optimization.

Dynamic Multiobjective Evolutionary Algorithm with Fuzzy Weighted Borda Count Method This paper introduces a novel dynamic multiobjective optimization algorithm. When a change occurs, the optimization algorithm has to give a true response to change. One of the most popular approaches of responding to change is to select some solutions randomly and remove them from the population. Since random selection results in deterioration of the algorithmu0027s performance, the wise selection is crucial. Recently, the Borda count method was applied to find the most appropriate solutions to be eliminated from the population. However, basic Borda suffers from the same weight of information before and after the change. In this paper, we propose a new weighted Borda count so that its parameters are tuned by fuzzy rules. Mamdani fuzzy rules have been employed to tune the weights and distinguish between information before and after the change. Finally, the u0027Change Effectu0027 is proposed to calculate the effect of the change on the solutions. The performance of the proposed algorithm is tested on standard functions and is compared with recent algorithms.