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ABSTRACTDifferential Evolution (DE) is a powerful and versatile algorithmfor numerical optimization, but one of its downsides is its numberof parameters that need to be tuned. Multiple techniques have beenproposed to self-adapt DE's parameters, with L-SHADE being oneof the most well established in the literature. This work presentsthe A-SHADE algorithm, which modifies the population size reductionschema of L-SHADE, and also EB-A-SHADE, which applies amutation strategy hybridization framework todoi:10.14210/cotb.v11n1.p351-358 fatcat:e7a2oo6hkzdezpwt6a3dlivb4u