Meta-learning Based Optimization of Metabolic Pathway Data-Mining Inference System

Arredondo, Tomás; Ormazábal, Wladimir; Candel, Diego C.; Creixell, Werner

doi:10.1007/978-3-642-21827-9_19

Cited by 1 publication

(1 citation statement)

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“…Optimization and networks design applications are very broad, traversing from a specific area and ranging from engineering design (Schwabacher et al 1998;Coelho and Mariani 2008;Kashan 2011), process optimization (Egea et al 2010;Joshi and Pande 2011;Kwak and Kim 2012), scheduling system (Andersson et al 2007;Frantzénl et al 2011;Skobelev 2011), routing and flow control in networks and networking (Madan et al 2007;Shakkottai and Srikant 2007;Minoux 2010), to service oriented applications in finance (He et al 2008;Leibfritz and Maruhn 2009;Pennanen 2011), healthcare (Harrell and Lange 2001;Bagirov and Churilov 2003;José et al 2011), and bioinformatics (Hernandez and Kambhampati 2004;Nebro et al 2008;Arredondo et al 2011). For example, in the formulation of the scheduling system, optimization can be used to determine the course of vehicle systems to the various destinations, to determine the scheduling of jobs in the factory, scheduling lectures at universities, creating timetable, computer network design and planning strategies for finding an optimal decision.…”

Section: Optimization and Network Designmentioning

confidence: 98%

An introduction to swarming robotics: application development trends

2013

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Animals help to sustain the environment's life cycle and ecosystem. Without human intervention, these creatures carry out their 'spontaneous routine' jobs and contribute towards balance in nature. Any natural system that congregates as a result of some form of collective intelligence of nature is also known as swarm intelligence (SI). This metaphor inspires a variety of techniques to solve the problem of calculating, in most cases dealing with optimization problems and has sparked interest amongst scientists. It is very trying for a new researcher to understand the whole concept of swarming robotics (SR) and optimization algorithm (i.e. realizing the idea from animal's perception to the SR application). In addition, the existing algorithms are computationally complicated, difficult to be understood by beginners as there are too many parameters. Thus, in this paper, we simplify the three branches of the main applications which are frequently used for SI namely: (1) optimization and networks design, (2) prediction and forecasting, and (3) SR. This paper summarizes the basic understanding overview of swarming robotics and discusses their basic concepts and principles.

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