A Novel Approach to Automatic Music Composing: Using Genetic Algorithm
نویسندگان
چکیده
Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering entropy of the notes distribution as a factor of fitness function and developing mutation and crossover functions based on harmonic rules and trying to keep the melodies intact during these processes would result in a musical piece pleasant to human ears and interesting for human mind. This algorithm does not have the constraints of the previous algorithms. Restraining mutation and crossover functions with a goal of producing melodies based on acceptable melodies composed by humans, this algorithm is not bound to any genre, instrument or melody. The experimental results of this approach show that it is near to the human composing and the results produced from it are more acceptable than the ones produced by its predecessors.
منابع مشابه
A Genetic Algorithm Approach to Collaborative Music Creation on a Multi-Touch Table
Multi-touch interfaces provide new opportunities for collaborative music composing. In this report, an approach using genetic algorithms to evolve musical beats in a collaborative setting is presented. A prototype using a multitouch interface is developed and evaluated.
متن کاملAn Optimized PID for Capsubots using Modified Chaotic Genetic Algorithm (RESEARCH NOTE)
This paper proposes a design for a mesoscale capsule robot which can be used in gaining diagnostic data and delivering medical treatment in inaccessible parts of the human body. A novel approach is presented for the capsule robot control: A PID-controlled closed-loop approach. A modified chaotic genetic algorithm will be used to optimize the coefficients of PID controller. Then, simulation will...
متن کاملImproved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring
In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...
متن کاملA novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem
Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this pap...
متن کاملA Novel QSAR Model for the Evaluation and Prediction of (E)-N’-Benzylideneisonicotinohydrazide Derivatives as the Potent Anti-mycobacterium Tuberculosis Antibodies Using Genetic Function Approach
Abstract A dataset of (E)-N’-benzylideneisonicotinohydrazide derivatives as a potent anti-mycobacterium tuberculosis has been investigated utilizing Quantitative Structure-Activity Relationship (QSAR) techniques. Genetic Function Algorithm (GFA) and Multiple Linear Regression Analysis (MLRA) were used to select the descriptors and to generate the correlation QSAR models that relate the Mi...
متن کامل