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ЭВОЛЮЦИОННОЕ МОДЕЛИРОВАНИЕ МУЗЫКИ: ПРИНЦИПЫ, ПОДХОДЫ, ПРОБЛЕМЫ

ЭВОЛЮЦИОННОЕ МОДЕЛИРОВАНИЕ МУЗЫКИ: ПРИНЦИПЫ, ПОДХОДЫ, ПРОБЛЕМЫ

Abstract

В публикуемой статье отмечено, что возникновение и развитие всех известных биологических систем становится возможным благодаря эволюционным процессам. Исходя из этого, устойчивость, гибкость и разнообразие видов многоуровневой организации, присущей упомянутым системам, служат мощным стимулом для использования принципов естественной эволюции в различных областях науки и художественного творчества. К примеру, в музыкальном искусстве применяются методы, характерные для биологической эволюции на генетическом уровне: скрещивание, мутация, отбор; основой для эволюционного моделирования музыки становится геном. Применительно к зародившемуся на рубеже XX-XXI столетий новому направлению музыкального творчества эволюционной музыкальной композиции, независимо от выбранного способа работы с материалом, определение функции приспособленности (фитнес-функции), позволяющей достигать художественно привлекательных или хотя бы стилистически корректных результатов, является сложной задачей. Для решения соответствующих проблем используются различные подходы от интерактивного вычисления функции и методов машинного обучения до применения заранее определённых строгих правил. В настоящей статье, на основе анализа сильных и слабых сторон эволюционного моделирования музыки (сообразно сегодняшнему этапу развития технологий и музыкальной науки), предлагается типологическая характеристика методов формирования и структурной организации фитнес-функции.

In the published article it is mentioned that beginnings and development of all noted biological systems become feasible owing to evolution processes. Hence the robustness, flexibility and diversity of poly-level organization kinds which are inherent in the named systems are the great stimulus for using of natural evolution principles in different areas of science and artistic work. As example some methods typical for biological evolution on a genetic level, such as crossover, mutation and selection, are conformed to musical art; thus, the genome becomes the base of evolution modelling of music. As applied to a new direction of musical art (that was conceived on the boundary of the XX-XXIst centuries) the evolutionary music composition, irrespective of the work with a ma terial ways, the definition of fitness function which favours to achieve the artistically inviting or even if stylistically correct results is a difficult problem. Different approaches have been used to resolve this problem: from interactive computation and machine learning to application of premeditated strict rules. This article is based on analysis of the advantages and disadvantages of evolutionary modelling of music according to the current stage of technologies and musicology development. The article presents a typological description of methods for construction of fitness function and its key structures.

Keywords

ИНТЕРАКТИВНАЯ МУЗЫКА,ЭВОЛЮЦИОННОЕ МОДЕЛИРОВАНИЕ,ФУНКЦИЯ ПРИСПОСОБЛЕННОСТИ,ГЕНЕТИЧЕСКИЙ АЛГОРИТМ,ГЕНЕТИЧЕСКОЕ ПРОГРАММИРОВАНИЕ,INTERACTIVE MUSIC,EVOLUTION MODELLING,FITNESS FUNCTION,GENETIC ALGORITHM,GENETIC PROGRAMMING

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average