EvoComposer: An Evolutionary Algorithm for 4-Voice Music Compositions

2020 ◽  
Vol 28 (3) ◽  
pp. 489-530
Author(s):  
R. De Prisco ◽  
G. Zaccagnino ◽  
R. Zaccagnino
Keyword(s):  
Evolutionary Algorithm ◽  
Specific Problem ◽  
Evaluation Study ◽  
Special Relationship ◽  
Evaluation Function ◽  
Nsga Ii ◽  
Composing Process ◽  
Measures Of Performance ◽  
Large Corpus

Evolutionary algorithms mimic evolutionary behaviors in order to solve problems. They have been successfully applied in many areas and appear to have a special relationship with creative problems; such a relationship, over the last two decades, has resulted in a long list of applications, including several in the field of music. In this article, we provide an evolutionary algorithm able to compose music. More specifically we consider the following 4-voice harmonization problem: one of the 4 voices (which are bass, tenor, alto, and soprano) is given as input and the composer has to write the other 3 voices in order to have a complete 4-voice piece of music with a 4-note chord for each input note. Solving such a problem means finding appropriate chords to use for each input note and also finding a placement of the notes within each chord so that melodic concerns are addressed. Such a problem is known as the unfigured harmonization problem. The proposed algorithm for the unfigured harmonization problem, named EvoComposer, uses a novel representation of the solutions in terms of chromosomes (that allows to handle both harmonic and nonharmonic tones), specialized operators (that exploit musical information to improve the quality of the produced individuals), and a novel hybrid multiobjective evaluation function (based on an original statistical analysis of a large corpus of Bach's music). Moreover EvoComposer is the first evolutionary algorithm for this specific problem. EvoComposer is a multiobjective evolutionary algorithm, based on the well-known NSGA-II strategy, and takes into consideration two objectives: the harmonic objective, that is finding appropriate chords, and the melodic objective, that is finding appropriate melodic lines. The composing process is totally automatic, without any human intervention. We also provide an evaluation study showing that EvoComposer outperforms other metaheuristics by producing better solutions in terms of both well-known measures of performance, such as hypervolume, [Formula: see text] index, coverage of two sets, and standard measures of music creativity. We conjecture that a similar approach can be useful also for similar musical problems.

scholarly journals HYBRID NSGA-II OPTIMIZATION FOR IMPROVING THE THREE-TERM BP NETWORK FOR MULTICLASS CLASSIFICATION PROBLEMS

2015 ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
Siti Mariyam Shamsuddin ◽  
Sultan Noman Qasem
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Classification Performance ◽  
Classification Problems ◽  
Nsga Ii ◽  
Bp Network ◽  
Multiobjective Evolutionary Algorithm

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.  

Election Poster Persuasion

2018 ◽  
Vol 49 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Malte Schott ◽  
Jule Wolf
Keyword(s):  
Political Parties ◽  
Policy Evaluation ◽  
Political Party ◽  
Need For Cognition ◽  
Evaluation Study ◽  
Additional Measure ◽  
Political Preferences ◽  
Policy Statements ◽  
Voting Intentions

Abstract. We examined the effect of presenting unknown policy statements on German parties’ election posters. Study 1 showed that participants inferred the quality of a presented policy from knowledge about the respective political party. Study 2 showed that participants’ own political preferences influenced valence estimates: policy statements presented on campaign posters of liked political parties were rated significantly more positive than those presented on posters of disliked political parties. Study 3 replicated the findings of Study 2 with an additional measure of participants’ need for cognition. Need for cognition scores were unrelated to the valence transfer from political parties to policy evaluation. Study 4 replicated the findings of Studies 2 and 3 with an additional measure of participants’ voting intentions. Voting intentions were a significant predictor for valence transfer. Participants credited both their individually liked and disliked political parties for supporting the two unknown policies. However, the credit attributed to the liked party was significantly higher than to the disliked one. Study 5 replicated the findings of Studies 2, 3, and 4. Additionally, participants evaluated political clubs that were associated with the same policies previously presented on election posters. Here, a second-degree transfer emerged: from party valence to policy evaluation and from policy evaluation to club evaluation. Implications of the presented studies for policy communications and election campaigning are discussed.

scholarly journals Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm

2020 ◽  
Vol 20 (3) ◽  
pp. 45-63
Author(s):  
Andranik S. Akopov ◽  
Levon A. Beklaryan ◽  
Armen L. Beklaryan
Keyword(s):  
Genetic Algorithm ◽  
Individual Characteristics ◽  
Nsga Ii ◽  
Human Agent ◽  
Performance Metric ◽  
Novel Approach ◽  
Distributed Processes ◽  
Real Coded Genetic Algorithm ◽  
Decision Making System

AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., -MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.

scholarly journals THE APPLICATION-BASED ANALYSIS OF QUESTIONS ITEM QUALITY IN JUNIOR HIGH SCHOOL

2020 ◽  
Vol 6 (2) ◽  
pp. 140-148
Author(s):  
Rabiatul Adawiah
Keyword(s):  
High School ◽  
Junior High School ◽  
Junior High ◽  
Answer Sheet ◽  
Evaluation Study ◽  
Poor Quality ◽  
Multiple Choice Questions ◽  
Civics Education ◽  
Level Of Difficulty

The activity of analyzing the items is one of the obligations for each teacher in an effort to improve the quality of the questions. However, for Civics Education teachers, this has never been done especially for questions created by the Subject Teachers' Consultation which are used for the end-semester assessment. The purpose of this study was to determine the quality of the questions based on distinguishing feature, level of difficulty, and effectiveness of the distractor. This study is an evaluation study of 50 items of  Civics Education Subject test in Banjarmasin, totaling 50 questions in the form of multiple choice questions at the end-semester test, academic year 2019/2020. The data collected are in the form of: (1) final exams question sheet, (2) question answer key sheet, and (3) students’ answer sheet. All data is obtained by documentation techniques. Data analysis uses the AnBuso version 8.0 application. The criteria for determining the quality of items are: (a) questions are considered good if the distinguishing feature is good/good enough, the level of difficulty is medium and all alternative answers are effective, (b) revision of alternative answers, if the distinguishing feature is good/good enough and the level of difficulty is medium, but the alternative there are ineffective answers, (c) good enough, if the distinguishing feature is good/good enough but the level of difficulty is easy/difficult, and (d) not good, if the distinguishing feature is not good.  The results of this study indicate that the questions used for the end-semester test at Junior High School in Banjarmasin are 50 % of poor quality.

scholarly journals Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multi-Objective Evolutionary Algorithm

2021 ◽  
pp. 1-34
Author(s):  
Joost Huizinga ◽  
Jeff Clune
Keyword(s):  
Evolutionary Algorithm ◽  
Function Optimization ◽  
Stepping Stones ◽  
Nsga Ii ◽  
Navigation Problem ◽  
Multi Objective ◽  
Robot Locomotion ◽  
Important Challenge ◽  
Maze Navigation ◽  
Multimodal Problems

Abstract An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of sub-tasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multi-Objective Evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III and ϵ-Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks and a simulated robot maze navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

scholarly journals Technical note: the caRamel R package for Automatic Calibration by Evolutionary Multi Objective Algorithm

2019 ◽  
Author(s):  
Céline Monteil ◽  
Fabrice Zaoui ◽  
Nicolas Le Moine ◽  
Frédéric Hendrickx
Keyword(s):  
Stable Solution ◽  
R Package ◽  
Technical Note ◽  
Main Function ◽  
Nsga Ii ◽  
Multiple Criteria Optimization ◽  
Multi Objective ◽  
Study Case ◽  
Nondominated Sorting

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment to calibrate these parameters. A multiobjective calibration allows to find a compromise between different goals by defining a set of optimal parameters. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex method (MEAS) and the Nondominated Sorting Genetic Algorithm II (ε-NSGA-II algorithm). The optimizer was initially developed for the calibration of hydrological models but can be used for any environmental model. The main function of the package, caRamel(), requires to define a multi-objective calibration function as well as bounds on the variation of the underlying parameters to optimize. CaRamel is well adapted to complex modelling. As an example, caRamel converges quickly and has a stable solution after 5,000 model evaluations with robust results for a real study case of a hydrological problem with 8 parameters and 3 objectives of calibration. The comparison with another well-known optimizer (i.e. MCO, for Multiple Criteria Optimization) confirms the quality of the algorithm.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem

2016 ◽  
pp. 170-194
Author(s):  
Arion de Campos Jr. ◽  
Aurora T. R. Pozo ◽  
Silvia R. Vergilio
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Optimization Algorithms ◽  
Web Service Composition ◽  
Nsga Ii ◽  
Preference Relations ◽  
Multi Objective ◽  
Composition Problem

The Web service composition refers to the aggregation of Web services to meet customers' needs in the construction of complex applications. The selection among a large number of Web services that provide the desired functionalities for the composition is generally driven by QoS (Quality of Service) attributes, and formulated as a constrained multi-objective optimization problem. However, many equally important QoS attributes exist and in this situation the performance of the multi-objective algorithms can be degraded. To deal properly with this problem we investigate in this chapter a solution based in many-objective optimization algorithms. We conduct an empirical analysis to measure the performance of the proposed solution with the following preference relations: Controlling the Dominance Area of Solutions, Maximum Ranking and Average Ranking. These preference relations are implemented with NSGA-II using five objectives. A set of performance measures is used to investigate how these techniques affect convergence and diversity of the search in the WSC context.

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