简介：Inmanyreal-worldapplicationsofevolutionaryalgorithms,thefitnessofanindividualrequiresaquantitativemeasure.Thispaperproposesaself-adaptivelinearevolutionaryalgorithm(ALEA)inwhichweintroduceanovelstrategyforevaluatingindividual'srelativestrengthsandweaknesses.Basedonthisstrategy,searchingspaceofconstrainedoptimizationproblemswithhighdimensionsfordesignvariablesiscompressedintotwo-dimensionalperformancespaceinwhichitispossibletoquicklyidentify'good'individualsoftheperformanceforamultiobjectiveoptimizationapplication,regardlessoforiginalspacecomplexity.Thisisconsideredasourmaincontribution.Inaddition,theproposednewevolutionaryalgorithmcombinestwobasicoperatorswithmodificationinreproductionphase,namely,crossoverandmutation.Simulationresultsoveracomprehensivesetofbenchmarkfunctionsshowthattheproposedstrategyisfeasibleandeffective,andprovidesgoodperformanceintermsofuniformityanddiversityofsolutions.

简介：这笔记与Fbeing考虑答案到概括Sylvester矩阵方程AV+BW=VF一个任意的矩阵，在V和W是坚定的矩阵的地方。在Kronecker地图的帮助下，这个矩阵方程的一个明确的参量的答案被建立。建议答案拥有一种很简单、整洁的形式，并且允许矩阵F未经决定。