Interactive Evolutionary Computation with Fitness Noise and Its Convergence Robustness

Noise is an important factor that influences the performance of evolutionary computation(EC).Much research on noise was reported in traditional EC,but less in IEC(interactive evolutionary computation).The definition,source,type of noise and methods to deal with noise in EC are reviewed firstly.Secondly,related with the rational user in IEC,the convergence robustness against fitness noise in IEC is studied.Mapping among spaces, dominating relationship and convergence in IEC are discussed to establish bases for two theorems:Strong condition theorem and weak condition theorem.These two theorems imply that the noise caused by the rational user will not prevent the algorithm from converging to the global optima.Thirdly,as the successive issue,the conclusions that the effective fitness scaling method is part of the weak condition and the user preference is the true fitness in IEC are discussed.The narrow definition of fitness noise based on the weak condition is also given.The experimental results validate the theorems,and the results establish a necessary foundation for future research.

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