| 摘要: | Music exerts a ubiquitous influence on human cultures and daily lives. Composing music is deemed rather complicated because it involves various factors (e.g., instruments, melodies, rhythm, and chords) needed to be well coordinated for creating harmony, tension, and emotions. This study focuses on an important domain of evolutionary composition methods, i.e., rule-based evolutionary systems. By including music knowledge, this type of systems is able to compose quality music fully automatically. Many of these systems applied weighted rules for music quality evaluation. The elaborately crafted rules and weights power the evolutionary composition systems, yet unintentionally limits extendibility of the systems. Based on the existing studies, we reorganized the music knowledge involved and proposed the deparameterized evaluation function (DEF) to improve the flexibility and transparency of rule-based evaluation. As a case study, the DEF is applied to composing bossa nova music. Our evolutionary system for bossa nova is satisfactory in music quality and acceptable in efficiency. The transparency of the DEF allows human's validation, and furthermore, the development of advanced memetic algorithms. Evolutionary multitasking (EMT), an influential paradigm of memetic computing, features the ability to solve multiple optimization tasks in parallel. A state-of-the-art EMT algorithm is the multifactorial evolutionary algorithm (MFEA). Despite of its remarkable performance, several issues are found impeding the search. This study accordingly proposed an advanced version named the multifactorial evolutionary algorithm with resource reallocation (MFEARR), which adaptively alters the searching behavior to improve the usage of evaluation resources. Tested on the multitask optimization (MTO) benchmark suite, the proposed MFEARR shows significant improvement in both efficiency and effectiveness. Moreover, the MFEARR possesses the potential to improve the traditional evolutionary composition systems in composing polyphonic (multi-melody) music because different melody composition tasks usually share some common ground in evaluation criteria. In other words, the tasks are mutually correlated and presumed to facilitate one another through EMT. To exploit this advantage, a novel MTO problem is formulated for polyphonic music composition and the evolutionary multitask composer (EMTC) is developed to solve this problem. The empirical results support the effectiveness of the MTO problem formulation and the efficiency of the EMTC in composing polyphonic music.
音樂在文化與日常生活中，有著無所不在的影響力。音樂創作因為需同時考量諸多要素而被視為相當複雜的工作。此研究著重在演化式作曲的一個重要的領域──基於規則評估的演化系統。這類系統藉由納入音樂知識來自動地創作優質的音樂作品。許多這樣的系統採用具權重的規則來評估音樂品質。雖然那般精心設計的規則和權重能驅動演化作曲系統，然而卻限制了系統的使用彈性。基於現存的研究，我們重新組織相關的音樂知識來設計一個去參數化之評估函數，以改善基於規則評估的透明性與彈性。此去參數化之評估函數亦被應用於自動創作bossa nova音樂作為一個案例分析。我們的演化系統可以有效率地創作令人滿意的bossa nova音樂作品。去參數化之評估函數的透明性允取使用者進一步為其開發進階的瀰因演算法 (memetic algorithm)。作為瀰因計算中頗具影響力的領域，演化多工 (evolutionary multitasking) 能平行化地解決多個最佳化工作。其中一個最先進的演化多工演算法為「多因演化演算法」 (multifactorial evolutionary algorithm)。儘管它擁有出色的效能，它仍有些許問題可能對搜尋過程帶來不利的影響。此研究因此提出「具資源再分配之多因演化算法」 (multifactorial evolutionary algorithm with resource reallocation) 作為一個改良版本。這個改良版本能因應搜尋當下狀況自動調節搜尋行為來改善評估資源的利用率。「具資源再分配之多因演化算法」在一系列的演化多工的效能測試問題上，都能顯著地提升搜尋效能。除此之外，在創作多聲部音樂的問題中，它還具有改良傳統演化作曲系統的潛力。這類問題中包含多個旋律創作工作，而由於這些工作具有某種程度的關聯性，所以可以透過演化多工來促進工作之間的相輔相成。為了利用這個優勢，我們針對多聲部音樂作曲制訂了一個新穎的多工最佳化問題，並開發了「演化多工作曲系統」 (evolutionary multitask composer) 來解決這樣的多工最佳化問題。實驗結果驗證了這個問題制訂的有效性，以及演化多工作曲系統的作曲效率。 |
