Items filtered by date: January 2015

The EvoSpace Model for Pool-Based Evolutionary Algorithms

Published in Journal Articles

Abstract

This work presents the EvoSpace model for the development of pool-based evolutionary algorithms (Pool-EA). Conceptually, the EvoSpace model is built around a central repository or population store, incorporating some of the principles of the tuple-space model and adding additional features to tackle some of the issues associated with Pool-EAs; such as, work redundancy, starvation of the population pool, unreliability of connected clients or workers, and a large parameter space. The model is intended as a platform to develop search algorithms that take an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet or volunteer computing resources within a local network. A comprehensive analysis of the model at both the conceptual and implementation levels is provided, evaluating performance based on efficiency, optima found and speedup, while providing a comparison with a standard EA and an island-based model. The issues of lost connections and system parametrization are studied and validated experimentally with encouraging results, that suggest how EvoSpace can be used to develop and implement different Pool-EAs for search and optimization.

  1. Mario García-Valdez, Leonardo Trujillo, Juan-J Merelo, Francisco Fernández de Vega and Gustavo Olague. The EvoSpace Model for Pool-Based Evolutionary Algorithms. Journal of Grid Computing, pages 1-21, 2014. URL, DOI BibTeX

    @article{,
    	year = 2014,
    	issn = "1570-7873",
    	journal = "Journal of Grid Computing",
    	doi = "10.1007/s10723-014-9319-2",
    	title = "The EvoSpace Model for Pool-Based Evolutionary Algorithms",
    	url = "http://dx.doi.org/10.1007/s10723-014-9319-2",
    	publisher = "Springer Netherlands",
    	keywords = "Pool-based evolutionary algorithms; Distributed evolutionary algorithms; Heterogeneous computing platforms for bioinspired algorithms; Parameter setting",
    	author = "García-Valdez, Mario and Trujillo, Leonardo and Merelo, Juan-J and Fernández de Vega, Francisco and Olague, Gustavo",
    	pages = "1-21",
    	language = "English"
    }
    

Read more...

Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

Published in Journal Articles

Abstract

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Moreover, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

  1. Victor H Diaz-Ramirez, Andres Cuevas, Vitaly Kober, Leonardo Trujillo and Abdul Awwal. Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization. Optics Communications 338:77–89, 2015. URL, DOI BibTeX

    @article{Diaz-Ramirez2015,
    	author = "Diaz-Ramirez, Victor H. and Cuevas, Andres and Kober, Vitaly and Trujillo, Leonardo and Awwal, Abdul",
    	doi = "10.1016/j.optcom.2014.10.038",
    	file = ":home/emigdio/Documents/Mendeley Desktop/2015/Diaz-Ramirez et al. - 2015 - Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimiza.pdf:pdf",
    	issn = 00304018,
    	journal = "Optics Communications",
    	keywords = "Combinatorial optimization,Composite correlation filters,Multi-objective evolutionary algorithm,Object recognition",
    	pages = "77--89",
    	publisher = "Elsevier",
    	title = "{Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization}",
    	url = "http://linkinghub.elsevier.com/retrieve/pii/S0030401814009547",
    	volume = 338,
    	year = 2015
    }
    
Read more...
Subscribe to this RSS feed
Feedback