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Treffer: Computación Evolutiva Descentralizada de Modelo Híbrido usando Blockchain y Prueba de Trabajo de Optimización

Title:
Computación Evolutiva Descentralizada de Modelo Híbrido usando Blockchain y Prueba de Trabajo de Optimización
Authors:
Consultor de tesis, Pabón Burbano, María Constanza, Bastidas Caicedo, Harvey Demian
Source:
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Publisher Information:
Pontificia Universidad Javeriana Ingeniería Maestría en ingeniería con énfasis en Ingeniería de Sistemas y Computación 2018-01 2018-10-24T15:09:00Z 2018-10-24T15:09:00Z
Document Type:
E-Ressource Electronic Resource
Index Terms:
Facultad de Ingeniería, Ingeniería de Sistemas y Computación, Optimization, Evolutionary Computation, Neuroevolution, Genetic Algorithm, Peer-to-Peer, Blockchain, Proof of Work, Reinforcement Learning, Decentralized Networks, Distributed Computing, info:eu-repo/semantics/masterThesis, Tesis Maestría, info:eu-repo/semantics/publishedVersion
URL:
http://hdl.handle.net/11522/10811
Availability:
Open access content. Open access content
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http://creativecommons.org/licenses/by-nc-nd/2.5/co
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info:eu-repo/semantics/openAccess
Atribución-NoComercial-SinDerivadas 2.5 Colombia
Note:
Cali; Lat: 03 24 00 N degrees minutes; Lat: 3.4000 decimal degrees; Long: 076 30 00 W degrees minutes; Long: -76.5000 decimal degrees
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Other Numbers:
CKJAV oai:vitela.javerianacali.edu.co:11522/10811
Bastidas Caicedo, H. D. (2018, enero ) Computación Evolutiva Descentralizada de Modelo Híbrido usando Blockchain y Prueba de Trabajo de Optimización. Pontificia Universidad Javeriana, Cali.
1141245057
Contributing Source:
PONTIFICIA UNIVERSIDAD JAVERIANA CALI
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1141245057
Database:
OAIster

Weitere Informationen

Las técnicas de Computación Evolutiva (EC) como algoritmos genéticos, neuroevolución o swarm intelligence son métodos de optimización de parámetros de modelos matemáticos que se caracterizan por el uso de una población de soluciones candidatas que evolucionan en un espacio de búsqueda de una forma inspirada en los principios de la evolución biológica como la competencia, la selección o la reproducción. Existen varios modelos arquitecturales para implementar técnicas de EC en arquitecturas de procesamiento distribuido (dEC), los modelos con mejor tolerancia a fallas y menor costo comunicacional son los modelos híbridos basados en el modelo de Islas. Múltiples modelos para dEC se han implementado en frameworks o plataformas de software, pero las implementaciones encontradas tienen desventajas como que tienen baja tolerancia a fallas o les faltan mecanismos de trazabilidad que podrían ser deseables o necesarios para algunas aplicaciones. Para contrarrestar las desventajas mencionadas, el blockchain y la prueba de trabajo criptográfica (CPoW) son tecnologías para almacenar datos de trazabilidad de eventos en redes descentralizadas, pero con el requerimiento de una capacidad computacional adicional para la generación de una CPoW. En este documento se propone el uso de un blockchain con una prueba de trabajo de optimización (OPoW) para implementar un servicio de timestamping en una red descentralizada para optimización con dEC de modelos híbridos, usando para optimización la capacidad computacional que es usada para la generación de una prueba de trabajo criptográfica en otras redes basadas en blockchain como Bitcoin. El sacrificio de usar una prueba de trabajo útil es que la OPoW no es una función del contenido del bloque, sino solo del estado de optimización. Para la validación empírica de la OPoW, este documento describe el diseño de una plataforma de software descentralizada para implementar Algoritmos Evolutivos Distribuidos (dEA) utilizando el modelo de isla y los m
The Evolutionary Computation (EC) techniques such as genetic algorithms, neuroevolution or swarm intelligence are optimization methods characterized by using a population of candidate solutions that evolve in a search space in a way inspired by biological evolution principles like competition, selection or reproduction. There are several architectural models for implementing EC techniques in distributed processing architectures (dEC), the models with better fault tolerance and lower communicational cost are the hybrid models based on the so-called island model. Multiple models for dEC have been implemented in frameworks or software platforms, but the existing implementations are either programming language-specific, lack fault-tolerance or lack traceability features that could be desirable or required for some applications. For counteracting the mentioned disadvantages, the blockchain and the hash-based cryptographic proof-of-work (CPoW) are technologies that allow the storage of data for the traceability of events in a decentralized network, but with an additional computational capacity requirement for the generation of a CPoW. This document proposes the use of a blockchain with an optimization proof-of-work (OPoW) to implement a timestamping service in a decentralized network for dEC with hybrid models, using for optimization the computational capacity that is used for hash-based proof-of-work generation in other blockchain based networks like Bitcoin. The tradeoff of a useful proof of work is that the OPoW is not a function of the block contents but only of the optimization state. For the empirical validation of the proposed proof of work, this document describes the design of a decentralized software platform for implementing distributed Evolutionary Algorithms (dEA) using the island model and hybrid models. The proposed platform explores the use of a blockchain and an optimization proof-of-work to store a log of operations for traceability and synchronization o