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An Automatic Rhythm and Melody Composition System Considering User Parameters and Chord Progression Based on a Genetic Algorithm
In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.
Quality-Based Software Project Staffing and Scheduling with Project Deadline
Dongwon Seo, Donghwan Shin, Doo-Hwan Bae
Software project planning includes several processes for estimating the effort required to complete software project tasks, allocating human resources to tasks, and creating a project plan. Because software planning is becoming more complicated as the size of software projects grow, it is difficult for project managers to decide on a reasonable project plan. To help them, many automatic software project planning approaches have been proposed. The approaches all focus on minimizing project duration. But if the plan is simply to minimize the duration, without considering software quality, the plan can harm the eventual software quality. In our research to create a reasonable project plan, we consider software quality as well as duration of the project, by defining a software quality score. The project manager can plan the project to maximize software quality for a specific project duration.
ACDE²: An Adaptive Cauchy Differential Evolution Algorithm with Improved Convergence Speed
In this paper, an improved ACDE (Adaptive Cauchy Differential Evolution) algorithm with faster convergence speed, called ACDE2, is suggested. The baseline ACDE algorithm uses a "DE/rand/1" mutation strategy to provide good population diversity, and it is appropriate for solving multimodal optimization problems. However, the convergence speed of the mutation strategy is slow, and it is therefore not suitable for solving unimodal optimization problems. The ACDE2 algorithm uses a "DE/current-to-best/1" mutation strategy in order to provide a fast convergence speed, where a control parameter initialization operator is used to avoid converging to local optimization. The operator is executed after every predefined number of generations or when every individual fails to evolve, which assigns a value with a high level of exploration property to the control parameter of each individual, providing additional population diversity. Our experimental results show that the ACDE2 algorithm performs better than some state-of-the-art DE algorithms, particularly in unimodal optimization problems.
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