Application of Genetic Algorithms to Disaster Restoration under Uncertain Environments

2015 ◽  
pp. 281-304
Author(s):  
H. Furuta ◽  
K. Nakatsu ◽  
K. Ishibashi
Keyword(s):  
Genetic Algorithms ◽  
Uncertain Environments

Multi-Operator Genetic Algorithm for Dynamic Optimization Problems

2017 ◽  
Vol 6 (3) ◽  
pp. 139
Author(s):  
Al-khafaji Amen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Diversity ◽  
Dynamic Optimization Problems ◽  
Uncertain Environments ◽  
Convergence Problem ◽  
Royal Road

<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on the well-known dynamic optimization functions i.e., OneMax, Plateau, Royal Road and Deceptive. Empirical results show the superiority of the proposed algorithm when compared to state-of-the-art algorithms from the literature.</span>

Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches

2011 ◽  
pp. 344-368 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Genetic Algorithms ◽  
Nonlinear Programming ◽  
Production Planning ◽  
Numerical Solutions ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Pattern Search ◽  
Uncertain Environments ◽  
S Curve

In this chapter a solution is proposed to a certain nonlinear programming difficulties related to the presence of uncertain technological coefficients represented by vague numbers. Only vague numbers with modified s-curve membership functions are considered. The proposed methodology consists of novel genetic algorithms and a hybrid genetic algorithm pattern search (Vasant, 2008) for nonlinear programming for solving problems that arise in industrial production planning in uncertain environments. Real life application examples in production planning and their numerical solutions are analyzed in detail. The new method suggested has produced good results in finding globally near-optimal solutions for the objective function under consideration.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator
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