Multi-Objective Deep Network-Based Estimation of Distribution Algorithm for Music Composition

In the field of evolutionary algorithm music composition, most of the current researches focus on how to enhance environmental selection based on multi-objective evolutionary algorithms (MOEAs). However, the real music composition process defined as large-scale multi-optimization problems (LSMOP) involve the number of combinations, and the existing MOEA-based optimization process can be challenging to effectively explore the search space. To address this issue, we propose a new Multi-Objective Generative Deep network-based Estimation of Distribution Algorithm (MODEDA) based on dimensionality reduction in decision space. In order to alleviate the difficulties with dimensional transformation, we propose a novel solution search method that optimizes in the transformed space and ensures consistency between the pareto sets of the original problem. The proposed algorithm is tested on the knapsack problems and music composition experiments. The experimental results have demonstrated that the proposed algorithm has excellency in terms of its optimization performance and computational efficiency in LSMOP.