Modeling Simple Genetic Algorithms for Permutation Problems

1995 ◽  
pp. 163-184 ◽  
Author(s):  
Darrell Whitley ◽  
Nam-Wook Yoo
Keyword(s):  
Genetic Algorithms ◽  
Simple Genetic Algorithms ◽  
Permutation Problems

GA-Based Multi-Objective Fuzzy Matter-Element Optimization

2002 ◽  
Author(s):  
Yanwei Zhao ◽  
Ertian Hua ◽  
Guoxian Zhang ◽  
Fangshun Jin
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problem ◽  
Fuzzy Optimization ◽  
Optimization Methods ◽  
Optimization Criterion ◽  
Multi Objective ◽  
Simple Genetic Algorithms ◽  
Solving Strategy ◽  
Crossover And Mutation ◽  
Better Than

The solving strategy of GA-Based Multi-objective Fuzzy Matter-Element optimization is put forward in this paper to the kind of characters of product optimization such as multi-objective, fuzzy nature, indeterminacy, etc. Firstly, the model of multi-objective fuzzy matter-element optimization is created in this paper, and then it defines the matter-element weightily and changes solving multi-objective optimization into solving dependent function K(x) of the single objective optimization according to the optimization criterion. In addition, modified adaptive macro genetic algorithms (MAMGA) are adopted to solve the optimization problem. It emphatically modifies crossover and mutation operator. By the comparing MAMGA with adaptive macro genetic algorithms (AMGA), not only the optimization is a little better than the latter, but also it reaches the extent to which the effective iteration generation is 62.2% of simple genetic algorithms (SGA). Lastly, three optimization methods, namely fuzzy matter-element optimization, linearity weighted method and fuzzy optimization, are also compared. It certifies that this method is feasible and valid.

scholarly journals Purposeful model parameters genesis in simple genetic algorithms

2012 ◽  
Vol 64 (3) ◽  
pp. 221-228 ◽  
Author(s):  
Maria Angelova ◽  
Krassimir Atanassov ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithms ◽  
Model Parameters ◽  
Simple Genetic Algorithms

Incorporating Twinkling in Genetic Algorithms for Global Optimization

2008 ◽  
Author(s):  
George S. Ladkany ◽  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithms ◽  
Global Optimization ◽  
Rate Of Convergence ◽  
Weighted Average ◽  
Distributed Arithmetic ◽  
Design Problems ◽  
Random Deviation ◽  
Engineering Design Problems ◽  
Crossover Phase ◽  
Simple Genetic Algorithms

Genetic algorithms have been extensively used as a reliable tool for global optimization. However these algorithms suffer from their slow convergence. To address this limitation, this paper proposes a two-fold approach to address these limitations. The first approach is to introduce a twinkling process within the crossover phase of a genetic algorithm. Twinkling can be incorporated within any standard algorithm by introducing a controlled random deviation from its standard progression to avoiding being trapped at a local minimum. The second approach is to introduce a crossover technique: the weighted average normally-distributed arithmetic crossover that is shown to enhance the rate of convergence. Two possible twinkling genetic algorithms are proposed. The performance of the proposed algorithms is successfully compared to simple genetic algorithms using various standard mathematical and engineering design problems. The twinkling genetic algorithms show their ability to consistently reach known global minima, rather than nearby sub-optimal points with a competitive rate of convergence.

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