Fuzzy fitness value interactive evolutionary optimization method used for indoor wall clock design

Abstract

The invention discloses a fuzzy fitness value interactive evolutionary optimization method used for indoor wall clock design. The fuzzy fitness value interactive evolutionary optimization method used for indoor wall clock design comprises the specific steps that: before the evolution is started, a system provides a design environment for a user so as to randomly generate initial evolutionary species; a person carries out single value evaluation for an individual through an interactive interface, and the system records the individual evaluation time; the system estimates an individual fitness value evaluated by the user based on the individual evaluation time and the single value evaluation value; the system clusters the species based on individual phenotype similarity and distributes unevaluated individual fussy fitness values; an individual selection adaptive value is built through the central value of the individual fussy fitness values and the individual phenotype similarity, and individual selection is finished by adopting a league method. The fuzzy fitness value interactive evolutionary optimization method used for indoor wall clock design efficiently reduces operation burden of designers as the designers only need to evaluate a small number of product types according to a single value; the system realizes estimation of the individual fuzzy fitness value evaluated by the user automatically at the background; and in comparison with the traditional fussy fitness value giving, inputting of modal particle is unnecessary and a half of the operation quantity is saved.

Description

technical field [0001] The invention belongs to the field of wall clock design, and particularly relates to an interactive evolution optimization method for indoor wall clock design, which can be used to guide users to design the appearance scheme of indoor wall clocks. Background technique [0002] Indoor wall clock is an important home furnishing product. In addition to the timing function, it is also a space decoration. A good wall clock appearance design can not only give people visual enjoyment, but also play a role in setting off the home atmosphere. Due to the rich design elements and strong artistry of the interior wall clock, quantitative objective functions cannot be used to measure and evaluate the evaluation scheme. However, the interactive evolution method can enhance the user's search ability, inspire users' design inspiration, and help users find a satisfactory design scheme. [0003] At present, the published strategies for applying interactive evolutionary ...

AI Technical Summary

Problems solved by technology

The above method for assigning individual fitness values ​​in large-scale populations expands the ability of users to evaluate individual fitness values, but there are still the following problems: First, the above method does not consider the fatigue problem in the user evaluation process enough. In order to obtain interval fitness values ​​and fuzzy fitness values, Users still need to increase the amount of operation; secondly, limited by the evaluation method, the proxy model can only approximate a limited number of modal particles, and the expression of preference is still insufficient; while estimating the fitness value assigned by non-users, the model error is relatively large

The above individual fitness value estimation methods provide guidance for the evolution of large-scale populations, but there are still the following problems: First, these fitness value estimation methods require the distance of individuals or the separability of individual gene patterns as the basis for estimation, but usually interactive The fitness value function in the genetic algorithm is unknown, and it is difficult to determine the mapping relationship between individual genotype and phenotype when the search space is more complex. Small differences in genotype often lead to significant differences in phenotype, so based on the genetic algorithm There is an error between the fitness value estimation of the distance and the individual phenotype

Secondly, using the surrogate model method to estimate the fitness value improves the optimization efficiency of the algorithm, but the calculation of individual similarity is still based on the genotype, so the model error still exists

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