Evolutionary music : a computational approach for algorithmic composition.

Este trabalho descreve uma abordagem para composição algorítmica baseada em algoritmos genéticos. São desenvolvidos dois módulos principais, que são os geradores melódico e harmônico. Um dos maiores problemas quando se usa algoritmos genéticos para evoluir melodias é criar uma medida esteticamente consciente de fitness. Neste trabalho, descreve-se uma nova abordagem com uma medida mínima de fitness na qual um conjunto de boas melodias é retornado no fim do processo. Logo depois, uma abordagem multiobjetivo é usada para harmonização da melodia. O algoritmo evolucionário multiobjetivo define mudanças de acordes com diferentes graus de simplicidade e dis- sonância. Experimentos foram feitos e comparados a julgamento humano dos resultados. As descobertas sugerem ser possível desenvolver funções de fitness que refletem intenções humanas para música.This work describes an approach for algorithmic composition based on genetic algorithms. Two main modules are described, which are the melodic and harmonic generators. One of the greatest problems when using genetic algorithms to evolve melodies is creating an aesthetically conscious measure of fitness. In this work, we describe a new approach with a minimum measure of fitness in which a set of good melodies is returned at the end of the process. Afterwards, a multiobjective approach is used for melody harmonization. This multiobjective evolutionary algorithm defines chord changes with di↵ering degrees of simplicity and dissonance. Experiments were held and compared to human judgment of the results. The findings suggest that it is possible to devise fitness functions which reflect human intentions for music

Aggregation Trees for visualization and dimension reduction in many-objective optimization.

This paper introduces the concept of Aggregation Trees for the visualization of the results of high-dimensional multi-objective optimization problems, or many-objective problems and as a means of performing dimension reduction. The high dimensionality of manyobjective optimization makes it difficult to represent the relationship between objectives and solutions in such problems and most approaches in the literature are based on the representation of solutions in lower dimensions. The method of Aggregation Trees proposed here is based on an iterative aggregation of objectives that are represented in a tree. The location of conflict is also calculated and represented on the tree. Thus, the tree can represent which objectives and groups of objectives are the most harmonic, what sort of conflict is present between groups of objectives, and which aggregations would be helpful in order to reduce the problem dimension

Memetic self-adaptive evolution strategies applied to the maximum diversity problem.

The maximum diversity problem consists in finding a subset of elements which have maximum diversity between each other. It is a very important problem due to its general aspect, that implies many practical applications such as facility location, genetics, and product design. We propose a method based on evolution strategies with local search and self-adaptation of the parameters. For all time limits from 1 to 300 s as well as for time to converge to the best solutions known, this method leads to better results when compared to other state-of-the-art algorithms

Optimizing two-level reverse distribution networks with hybrid memetic algorithms.

In a Two-Level Reverse Distribution Network, products are returned from customers to manufacturers through collection and refurbishing sites. The costs of the reverse chain often overtake the costs of the forward chain by many times. With some known algorithms for the problem as reference, we propose a hybrid memetic algorithm that uses linear programming and a heuristic for defining routes. Moreover, we describe heuristics for deciding locations, algorithms to define routes for the products, and problem-specific genetic operators. Memetic algorithms have returned the best results for all instances

Um algoritmo coevolutivo cooperativo para configuração de uma rede de sensores sem fio.

This work proposes a cooperative coevolutionary algorithm for design of a wireless sensor network considering complex network metrics. It is proposed a heuristic to find a network configuration such that its communication structure presents a small value for the average shortest path length and a high cluster coefficient. This configuration considers a cluster based network, where the cluster heads have two communication radii. We describe how the problem can be partitioned and how the fitness computation can be divided such that the cooperative coevolution model is feasible. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with two specifics complex network measurements allowing the reduction of energy consumption and the data transmission delay

A communitarian microgrid storage planning system inside the scope of a smart city.

In this paper (a substantial extension of the short version presented at REM2016 on April 19?21, Maldives [1]), multi-objective power dispatching is discussed in the scope of microgrids located in smart cities. The proposed system considers the use of Plug-in Electric Vehicle (PEV) and Unmanned Aerial Vehicle (UAV) as storage units. The problem involves distinct types of vehicles and a community, composed of small houses, residential areas and different Renewable Energy Resources. In order to highlight possibilities for power dispatching, the optimization of three distinct goals is considered in the analysis: mini/ microgrid total costs; usage of vehicles batteries; and maximum grid peak load. Sets of non-dominated solutions are obtained using a mathematical programming based heuristic (Matheuristic). By analyzing cases of study composed with up to 70 vehicles, we emphasize that PEVs and UAVs can effectively contribute for renewable energy integration into mini/microgrid systems. Smart cities policy makers and citizens are suggested to consider the proposed tool for supporting decision making for cities under development, guiding their choices for future investments on renewable energy resources

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data

The value of open-source clinical science in pandemic response: lessons from ISARIC

International audienc