BUSCADOR RECOLECTA

Description of the project

Description of the dataset

Methodology

• Step 1. Downloading the Quick, Draw! images in binary format.
• Step 2. Selection of 1200 images for each of the 345 classes (414000 elements in total).
• Step 3. The image size is 256x256 and they are converted from vector format into pixel format.
• Step 4. The 414000 images are divided into three sets: training (289800 elements), validation (62100 elements) and test (62100 elements).

Descripción del proyecto

Descripción del dataset

Metolodogía

• Etapa 1. Descarga de las imágenes de Quick, Draw! en formato binario.
• Etapa 2. Selección de 1200 imágenes por cada una de las 345 clases (414000 elementos en total).
• Etapa 3. El tamaño de las imágenes es de 256x256 y son transformadas de formato vectorial a formato de píxel.
• Etapa 4. Las 414000 imágenes son divididas en tres conjuntos: entrenamiento (289800 elementos), validación (62100 elementos) y test (62100 elementos).

Cellular evolutionary algorithms (cEAs) use structured populations whose evolutionary cycle is governed by local interactions among individuals. This helps to prevent the premature convergence to local optima that usually takes place in panmictic populations. The present work extends cEAs by means of a message passing phase whose main effect is a more effective exploration of the search space. The mutated offspring that potentially replaces the original individual under cEAs is considered under message passing cellular evolutionary algorithms (MPcEAs) as a message sent from the original individual to itself. In MPcEAs, unlike in cEAs, a new message is sent from the original individual to each of its neighbors, representing a neighbor’s mutated offspring whose second parent is selected from the neighborhood of the original individual. Thus, every individual in the population ultimately receives one additional candidate for replacement from each of its neighbors rather than having a unique candidate. Experimental tests conducted in the domain of real function optimization for continuous search spaces show that, in general, MPcEAs significantly outperform cEAs in terms of effectiveness. Specifically, the best solution obtained through MPcEAs has an importantly improved fitness quality in comparison to that obtained by cEAs.