Interactive Evolutionary Computation Approach to Permutation Flow Shop Scheduling Problem

Artificial intelligence and its subfields have be-come part of our everyday lives and eÿciently solve many problems that are very hard for us humans. But in some tasks, these methods strug-gle, while we, humans, are much better solvers with our intuition. Because of that, the ques-tion arises: why not combine intelligent methods with human skills and intuition? This paper pro-poses an Interactive Evolutionary Computation approach to the Permutation Flow Shop Schedul-ing Problem by incorporating human-in-the-loop in MAX-MIN Ant System through gamification of the problem. The analysis shows that combin-ing the evolutionary computation approach and human-in-the-loop leads to better solutions, sig-nificantly when the complexity of the problem in-creases.

Innovative Design of Intangible Cultural Heritage Elements in Fashion Design Based on Interactive Evolutionary Computation

The application of intangible cultural heritage cultural elements and traditional crafts in modern design, especially in modern fashion design, is not to flatter the public but to integrate the artistic language of intangible cultural heritage into modern fashion on the basis of deeply understanding the connotation of intangible cultural heritage and mastering its traditional crafts, so as to meet people’s demand for fashion and aesthetics. It can also promote the inheritance and development of traditional intangible cultural heritage culture and technology. The purpose of this study is to analyze the intangible cultural heritage elements in the innovative design of fashion design by using interactive evolutionary computation. According to the composition characteristics of cultural elements, this research uses interactive evolutionary calculation to analyze the current status of intangible cultural heritage elements in clothing design. Then, 30 fashion designers are selected to evaluate the design situation and judge the effect of the method on the design. The results show that the neural network has evaluated 36 generations, that is, 256 times of moderate value. Compared with the general IGA algorithm, adding neural network IGA can reduce the fatigue caused by user reference score and improve the quality of the optimal solution, and the cultural image attributes whose perception frequency is more than 50% are favored. It is concluded that the research method in the intangible cultural heritage elements in fashion design can improve user satisfaction and the effect is good. This research contributes to the application of intelligent algorithm in the field of fashion design.

Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user’s preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks.

Evolutionary Computation with User’s Preference for Solving Fuzzy Fitness Forecasting Problems

Interactive Evolutionary Computation (IEC) is a kind of human–machine interaction calculation method derived from evolutionary computation. The main problem of interactive evolutionary computation is that fitness noise can make evolution direction to deviate from user’s preferences because user’s evaluation has cognitive fluctuations and fatigue. To improve these deficiencies, this paper recommends a fuzzy fitness prediction method based on fuzzy gray model FGM (1,1) with a precise number fitness. First of all, the relationship between fitness noise intensity and the fitness function is proposed. Then, it suggests a linear programming of fuzzy fitness set width under the restriction of minimum noise intensity, which can calculate the fuzzy fitness prediction parameters. Finally, the fuzzy gray model forecasts the fuzzy fitness. The proposed method uses new computation of individual’s dominance relation and crowding distance to realize NSGA–II. The experimental results verify that this method has advantages in improving optimization quality, alleviating user’s fatigue and improving efficiency in exploration.