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This paper presents a novel differential evolution algorithm based on variable parameter search to solve real-parameter continuous function optimization problems. In order to provide differential evolution algorithm with local intensification capability, each trial individual is generated by a variable parameter search procedure using variable mutation scale factor and crossover rate as well as (possibly) variable mutation strategies. The novelty stems from the fact that while a pure differential evolution algorithm achieves global exploration during the search process, variable parameter search procedure intensifies the search around local minima by using traditional DE mutation and crossover operators as well as variable mutation strategies. The algorithm was tested using benchmark instances designed for a special session in CEC05 and other instaces from the literature. The experimental results show its highly competitive performance against the very recent differential evolution algorithm with local search by Noman and Iba in (IEEE Transaction on Evolutionary Computation, Vol. 12, No. 1, pp. 107-125, February 2008).