With the introduction of ChatGPT, the public's perception of AI-generated content (AIGC) has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects.

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment.Algorithmic composition aims to develop algorithms for music composition.Recently, algorithmic composition using artificial intelligence technologies received considerable attention.In particular, computational intelligence is widely used and achieves promising results in the creation of music.This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm.First, transform the MIDI music format files to digital data.Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern.Except for time series, the converted data also includes duration, pitch, and velocity.Second, using MC simulation to deal with them summed up their distribution law respectively.The two main control parameters are the value of discrete sampling and standard deviation.Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

The centroid is a special point determined by the mass distribution of the object, which is an important intrinsic parameter. The centroid balance is the key technology to ensure the safe operation of the aircraft. However, the limitations of the internal system structure of the aircraft will affect the centroid position, as well as attitude changes and fuel consumption of the aircraft, so it is necessary to develop the corresponding centroid balancing strategy to ensure that the aircraft fully exerts its stability and maneuverability. Firstly, the centroid change curve is drawn based on greedy strategy and mixed integer quadratic programming in this study, then the strategic optimization calculation model that can accurately describe the oil supply problem is established. Finally, using Gurobi to obtain a high-quality feasible solution within the effective time and ensure the centroid balance of the aircraft.

For many-objective optimization problems, the comparability of non-dominated solutions is always an essential and fundamental issue. Due to the inefficiency of Pareto dominance for many-objective optimization problems, various improved dominance relations have been proposed to enhance the evolutionary pressure. However, these variants have one thing in common that they treat each solution in a static manner, and the relations between any two solutions are just defined as a kind of static spatial adjacencies, resulting in the unquantifiable comparability. Different from them, this paper proposes a dominance degree metric, which treats solutions as different stages of a dynamic motion process. The dynamic motion process represents the continuous changes of the degree of one solution from Pareto dominating others to being Pareto dominated by others. Based on the dominance degree, this paper proposes a Many-Objective Evolutionary Algorithm based on Dominance Degree, in which the mating selection and environmental update strategies are redesigned accordingly. The proposed method is comprehensively tested with several state-of-the-art optimizers on two popular test suites and practical multi-point distance minimization problems. Experimental results demonstrate its effectiveness and superiority over other optimizers in terms of the convergence, diversity and spread.