Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with per...

I present hardware testing of an evolutionary algorithm (EA) known as distributed biogeography based optimization (DBBO). DBBO is an extended version of biogeography based optimization (BBO). Typically, EAs require a central computer to control the evaluation of candidate solutions to some optimization problem, and to control the sharing of information between those candidate solutions. DBBO, h...

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has ...

We report a successful case of living-donor lobar lung transplantation (LDLLT) for therapy-resistant broncho-bronchiolitis obliterans (BBO) after allogeneic hematopoietic stem cell transplantation (HSCT). Bronchiolitis obliterans (BO) is one of the late-onset noninfectious pulmonary complications that occur after allogeneic HSCT and is usually resistant to immunosuppressive therapy. A 17-year-o...

The computational discovery of DNA motifs for previously uncharacterized transcription factors in groups of co-regulated genes is a well-studied problem with a great deal of practical relevance to the biologist. In this paper, we applied an improved hybridization of adaptive Biogeography-Based Optimization (ABBO) with differential evolution (DE) approach, namely ABBO/DE/GEN, to predict motifs f...

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography, which involves the study of the migration of biological species between habitats. Previous work has shown that various migration models of BBO result in significant changes in performance. Sinusoidal migration models have been shown to provide the best performance so far. Motivated by biogeography th...

Due to dynamic network conditions, routing is the most critical part in WMNs and needs to be optimised. The routing strategies developed for WMNs must be efficient to make it an operationally self configurable network. Thus we need to resort to near shortest path evaluation. This lays down the requirement of some soft computing approaches such that a near shortest path is available in an afford...

The SEARCH (Search Envisioned As Relation & Class Hierarchizing) framework developed elsewhere (Kargupta, 1995a; Kargupta & Goldberg, 1995) ooered an alternate perspective toward blackbox optimization (BBO)|optimization in presence of little domain knowledge. The SEARCH framework investigates the conditions essential for transcending the limits of random enumerative search using a framework dev...

1550-7998=20 Inflation generically predicts a stochastic background of gravitational waves over a broad range of frequencies, from those accessible with cosmic microwave background (CMB) measurements, to those accessible directly with gravitational-wave detectors, like NASA’s Big-Bang Observer (BBO) or Japan’s Deci-Hertz Interferometer Gravitational-wave Observer (DECIGO), both currently under ...

Purpose – The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey Biogeography-Based Optimization (PPBBO) algorithm, with the objective of optimizing the whole UCAV system design process. Design/methodology/approach – The hybrid model of predator-prey theory and biogeography-based opti...