Abstract
Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem. Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable. This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals. On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model. The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective. In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings. And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained. In addition, the algorithm performance comparison is also completed. The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved. Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability.
Highlights
In today’s society, with the improvement of living standards, people’s consumption concept is gradually changing
In view of the above shortcomings, this paper studies how to improve the performance of the algorithm under the premise of reducing user fatigue with adoption of large-scale population evolution, and proposes a improved interactive genetic algorithm based on user preference adaptation value
The ceramic disk patterns of different styles can be generated by setting the style, tone collocation and structure weight respectively; (2) Individual similarity calculation integrates user preference information to make the calculation results more objective; (3) The selection of individuals for user evaluation has a uniform distribution in the contemporary population, which further reduces the burden of the algorithm; (4) In evaluating the fitness of individuals not assessed by the user, our method takes into account the weight of each component and the effect of component combination preferences
Summary
In today’s society, with the improvement of living standards, people’s consumption concept is gradually changing. In view of the above shortcomings, this paper studies how to improve the performance of the algorithm under the premise of reducing user fatigue with adoption of large-scale population evolution, and proposes a improved interactive genetic algorithm based on user preference adaptation value The advantage of this method is that the individual similarity calculation integrates the user’s preference information, which makes the calculation result more objective. The ceramic disk patterns of different styles can be generated by setting the style, tone collocation and structure weight respectively; (2) Individual similarity calculation integrates user preference information to make the calculation results more objective; (3) The selection of individuals for user evaluation has a uniform distribution in the contemporary population, which further reduces the burden of the algorithm; (4) In evaluating the fitness of individuals not assessed by the user, our method takes into account the weight of each component and the effect of component combination preferences. The fitness estimation formula of evolutionary individuals is put forward to make the calculation result more accurate
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