Espectrometria de massas aplicada aos estudos de biossíntese de alcalóides de Senna spectabilis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise estrutural das proteínas da seda da teia da aranha Nephila clavipes por uma abordagem proteômica

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

O direito penal e a manipulação genética de embrião humano

Pós-graduação em Direito - FCHS

Produção de fitase por Rhizopus microsporus var. microsporus: purificação, caracterização bioquímica e aplicação

Pós-graduação em Biotecnologia - IQ

Bone morphogenetic proteins: promising molecules for bone healing, bioengineering, and regenerative medicine

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic Algorithms (Binary and Real Codes) for the Optimisation of a Fermentation Process for Butanol Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic algorithm of chu and beasley for static and multistage transmission expansion planning

In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results achieved using it with the results using other meta-heuristics like tabu-search, simulated annealing, extended genetic algorithm and hibrid algorithms. © 2006 IEEE.

Optimal trajectory and solution of the inverse kinematics of a robotic manipulator by genetic algorithms

This paper presents the generation of optimal trajectories by genetic algorithms (GA) for a planar robotic manipulator. The implemented GA considers a multi-objective function that minimizes the end-effector positioning error together with the joints angular displacement and it solves the inverse kinematics problem for the trajectory. Computer simulations results are presented to illustrate this implementation and show the efficiency of the used methodology producing soft trajectories with low computing cost. © 2011 Springer-Verlag Berlin Heidelberg.

An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.

Rock outcrop orchids reveal the genetic connectivity and diversity of inselbergs of northeastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)