Music Melodies Suited to Multiple Users' Feelings Composed by Asynchronous Distributed Interactive Genetic Algorithm

Nomura, Kota; Fukumoto, Manabu

doi:10.4018/ijsi.2018040103

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…The radial basis function and calculation process of interactive genetic algorithm have good operability. In order to further apply interactive genetic algorithm to landscape design, the radial basis function and interactive hesitancy were analyzed, and the relevant indexes of landscape design were introduced into radial basis function [ 29 , 30 ]. The interactive hesitancy is optimized, and a new interactive genetic algorithm is obtained.…”

Section: Landscape Lighting Design and Research Based On Interactive ...mentioning

confidence: 99%

Exploration of Landscape Lighting Design Based on Interactive Genetic Algorithm

Shi

Zheng

2022

Computational and Mathematical Methods in Medicine

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There are many problems in the practical application of landscape lighting design. In order to solve these problems more specifically, based on the relevant theories of interactive genetic algorithm, radial basis function and hesitation degree are introduced into genetic algorithm. Through the analysis and processing of the data to get the optimized interactive genetic algorithm, the algorithm can analyze and optimize the landscape lighting design. Based on this model, the lighting design can be predicted and analyzed, and the prediction result is relatively good. Relevant studies show that the interactive genetic algorithm can be divided into three typical change stages according to the different results of intensity calculation, of which the first stage mainly presents the trend of gradual decline. The fluctuation phenomenon is obvious in the second paragraph. The third paragraph shows a gradual increasing trend of change. The corresponding relationship between the two fitness functions is obvious. With the increase of experts in independent variables, the corresponding fitness values show a trend of gradual decline on the whole. Through the calculation and analysis of five different indicators of landscape lighting by using interactive genetic algorithm, it can be seen that electrification has a relatively small impact on landscape lighting. The results of intelligent and environmental protection calculation are relatively high, and the corresponding range of change is relatively large, which shows that these two indicators are very important for improving the lighting design level of landscape. Finally, the model is verified by comparing data and model curves. Interactive genetic algorithm is very important to improve the lighting design of landscape, and the optimization model can be widely used in other fields.

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Section: Landscape Lighting Design and Research Based On Interactive ...mentioning

confidence: 99%

Exploration of Landscape Lighting Design Based on Interactive Genetic Algorithm

Shi

Zheng

2022

Computational and Mathematical Methods in Medicine

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“…Zhu, Wang, and Wang (2008) presented an IGA based on the KTH rule system (Friberg, Bresin, and Sundberg 2006) to create affective performances of pre-composed music pieces. Nomura and Fukumoto (2018) designed a distributed IGA to generate four-bar piano melodies with controllable brightness. Our method is different from IGAs because IGAs only provide a set of solutions at the end of the evolutionary process.…”

Section: Interactive Evolutionary Algorithmsmentioning

confidence: 99%

Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search

Ferreira

Mou

Whitehead

et al. 2022

AIIDE

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This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion. At every step of the decoding process, we use Predictor Upper Confidence for Trees (PUCT) to search for sequences that maximize the average values of emotion and quality as given by an emotion classifier and a discriminator, respectively. We use a language model as PUCT's policy and a combination of the emotion classifier and the discriminator as its value function. To decode the next token in a piece of music, we sample from the distribution of node visits created during the search. We evaluate the quality of the generated samples with respect to human-composed pieces using a set of objective metrics computed directly from the generated samples. We also perform a user study to evaluate how human subjects perceive the generated samples' quality and emotion. We compare PUCT against Stochastic Bi-Objective Beam Search (SBBS) and Conditional Sampling (CS). Results suggest that PUCT outperforms SBBS and CS in almost all metrics of music quality and emotion.

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“…With the continuous development of IGA in the design field, algorithm researchers put forward many new methods to solve the specific problems of IGA. In order to improve the IGA design efficiency, the IGA method of user collaboration was proposed [12,13]. In order to reduce user fatigue, a user cognitive model is constructed to replace user evaluation [14,15].…”

Section: Motivation and Significancementioning

confidence: 99%