Abstract
For the problem that interactive genetic algorithms lack a way of measuring uncertainty of comment, a method with grey level for uncertainty of individuals evolutionary is proposed in this paper in which the individual fitness is an interval. Through analyzing these fitness grey level, information reflecting the distribution of an evolutionary population is abstracted. Based on these, the adaptive probabilities of crossover and mutation operation of an evolutionary individual are proposed.
Highlights
Interactive genetic algorithms (IGAs), proposed in middle 1980s, are effective methods for solving optimization problems with implicit indices
When described the individual fitness with interval number, the f (xi (t)) and f (xi (t)) become preference data which reflect of the cognition
When human evaluation is a single numerical assessment, the fitness is still an uncertain number affected by noise, while the whitening number of f *(xi (t)) is only, the individual fitness can be considered a discrete measured grey number, and grey level is 0
Summary
Interactive genetic algorithms (IGAs), proposed in middle 1980s, are effective methods for solving optimization problems with implicit indices. One is that human evaluates evolutionary based on individuals fitness estimates and the other is extraction of cognitive information to guide the evolution of operating. Hao et al did it based on “the fitness” of gene sense units [2] For the latter idea, [3]presented the interval fitness evaluation, which using interval dominance select individuals who strongly reflects the ambiguity and progressive of human cognitive. These studies improve the algorithm performance, significantly reducing the people's fatigue, but they don’t give the explicit quantitative judgments of the uncertainty, and uncertainty is important for individual evaluation in interactive genetic algorithm, so exploring the uncertainty in interactive genetic algorithm is an important issue
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