scholarly journals Evolutionary computing in neuronal modeling

2019 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Genetic Algorithms ◽  
Action Potentials ◽  
Evolutionary Computing ◽  
Search Method ◽  
Neuronal Modeling

The efficacy of genetic algorithms in the design of models that model specific and experimental aspectsof action potentials in a wide variety of organisms is proven. A specific example of a plant actionpotential is used to illustrate the use of genetic algorithms in the search for parameters of models. Theefficiency of the genetic algorithms as a search method is in the short generation span of theconvergence of the algorithm.

scholarly journals Evolutionary Computing in Neuronal Modeling

2019 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Genetic Algorithms ◽  
Action Potentials ◽  
Evolutionary Computing ◽  
Search Method ◽  
Neuronal Modeling

The efficacy of genetic algorithms in the design of models that model specific and experimental aspectsof action potentials in a wide variety of organisms is proven. A specific example of a plant actionpotential is used to illustrate the use of genetic algorithms in the search for parameters of models. Theefficiency of the genetic algorithms as a search method is in the short generation span of theconvergence of the algorithm.

Initial value search method for multiple viewpoint stereo measurement using genetic algorithms

1995 ◽  
Vol 26 (8) ◽  
pp. 55-65
Author(s):  
Atsushi Nobiki ◽  
Hiroshi Naruse ◽  
Tetsuro Yabuta ◽  
Mitsuhiro Tateda
Keyword(s):  
Genetic Algorithms ◽  
Search Method ◽  
Initial Value

A Hybrid Genetic-Direct Search Algorithm for the Shape Optimization of Solid C-Frame Cross-Sections

2000 ◽  
Author(s):  
Ashraf O. Nassef ◽  
Hesham A. Hegazi ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithms ◽  
Shape Optimization ◽  
Cross Section ◽  
Random Search ◽  
Direct Search ◽  
Search Method ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
The Cross ◽  
Real Coded Genetic Algorithms

Abstract The hybridization of different optimization methods have been used to find the optimum solution of design problems. While random search techniques, such as genetic algorithms and simulated annealing, have a high probability of achieving global optimality, they usually arrive at a near optimal solution due to their random nature. On the other hand direct search methods are efficient optimization techniques but linger in local minima if the objective function is multi-modal. This paper presents the optimization of C-frame cross-section using a hybrid optimization algorithm. Real coded genetic algorithms are used as a random search method, while Nelder-Mead is used as a direct search method, where the result of the genetic algorithm search is used as the starting point of direct search. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal, circular and rectangular sections. The cross-sectional shape is represented by a non-uniform rational B-Splines (NURBS) in order to give it a kind of shape flexibility. The results showed that the use of Nelder-Mead with Real coded Genetic Algorithms has been very significant in improving the optimum shape of a solid C-frame cross-section subjected to a combined tension and bending stresses. The hybrid optimization method could be extended to more complex shape optimization problems.

Genetic Algorithms, Path Relinking, and the Flowshop Sequencing Problem

1998 ◽  
Vol 6 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Colin R. Reeves ◽  
Takeshi Yamada
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Simulated Annealing Algorithm ◽  
Search Method ◽  
Neighborhood Search ◽  
Search Technique ◽  
Permutation Flowshop ◽  
Sequencing Problem ◽  
Annealing Algorithm ◽  
Tabu Search Method

In a previous paper, a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighborhood search technique and a proven simulated annealing algorithm. However, recent results have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly.

scholarly journals Course scheduling optimization using genetic algorithm and tabu search

2021 ◽  
Vol 9 (3) ◽  
pp. 157-166
Author(s):  
Arif Amrulloh ◽  
Enny Itje Sela
Keyword(s):  
Higher Education ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Tabu Search ◽  
Search Method ◽  
Scheduling Optimization ◽  
Taboo Search ◽  
Course Scheduling ◽  
Study Programs ◽  
Tabu Search Method

Scheduling courses in higher education often face problems, such as the clashes of teachers' schedules, rooms, and students' schedules. This study proposes course scheduling optimization using genetic algorithms and taboo search. The genetic algorithm produces the best generation of chromosomes composed of lecturer, day, and hour genes. The Tabu search method is used for the lecture rooms division. Scheduling is carried out for the Informatics faculty with four study programs, 65 lecturers, 93 courses, 265 lecturer assignments, and 65 classes. The process of generating 265 schedules took 561 seconds without any scheduling clashes. The genetic algorithms and taboo searches can process quite many course schedules faster than the manual method.

Accuracy and Fitting-Time Minimization in the Reverse Engineering of Prismatic Features

1999 ◽  
Author(s):  
Ashraf O. Nassef ◽  
Ayman M. Ashraf ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithms ◽  
Reverse Engineering ◽  
Three Dimensional ◽  
Direct Search ◽  
Search Method ◽  
Direct Search Method ◽  
Simplex Search ◽  
Time Minimization ◽  
Engineering Models ◽  
Real Coded Genetic Algorithms

Abstract In many years of industry, it is desirable to create geometric models of existing objects for which no such models are available. Reverse engineering transforms real parts into engineering models and concepts. This paper presents an approach for fitting three-dimensional prismatic features using real-coded genetic algorithms. The approach is compared with the Nelder Meade Simplex search method as a variant of the traditional direct search method. The results show the superiority of the real-coded genetic algorithms over the traditional direct search method with respect to accuracy. The paper also concerns with the minimization of the fitting time through minimizing the number of objective function evaluations. This is done by optimizing the genetic algorithms parameters such as the number of times for which the various cross-overs and mutations should be applied.

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