InterCriteria Analysis of crossover and mutation rates relations in simple genetic algorithm

机译：简单遗传算法交叉和变异率关系的区间判据分析

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In this investigation recently developed InterCriteria Analysis (ICA) is applied to examine the influences of two main genetic algorithms parameters - crossover and mutation rates during the model parameter identification of S. cerevisiae and E. coli fermentation processes. The apparatuses of index matrices and intuitionistic fuzzy sets, which are the core of ICA, are used to establish the relations between investigated genetic algorithms parameters, from one hand, and fermentation process model parameters, from the other hand. The obtained results after ICA application are analysed towards convergence time and model accuracy and some conclusions about derived interactions are reported.

机译：在这项调查中，最近开发的InterCriteria分析（ICA）用于检查啤酒酵母和大肠杆菌发酵过程的模型参数识别过程中两个主要遗传算法参数的影响-交叉和突变率。作为ICA核心的指标矩阵和直觉模糊集的设备，用于一方面建立研究的遗传算法参数与另一方面建立发酵过程模型参数之间的关系。分析了应用ICA后获得的结果的收敛时间和模型精度，并报告了有关派生相互作用的一些结论。

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《Federated Conference on Computer Science and Information Systems》|2015年|419-424|共6页
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Angelova Maria; Roeva Olympia; Pencheva Tania;
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convergence; fuzzy set theory; genetic algorithms; matrix algebra; E coli fermentation processes; ICA; S cerevisiae fermentation processes; convergence time; crossover rates; genetic algorithms parameters; index matrices; intercriteria analysis; intuitionistic fuzzy sets; model accuracy; mutation rates; parameter identification; Biological system modeling; Correlation; Fuzzy sets; Genetic algorithms; Indexes; Mathematical model; Optimization;

机译：收敛性;模糊集理论;遗传算法;矩阵代数;大肠杆菌发酵过程; ICA;酿酒酵母发酵过程;收敛时间;交叉率;遗传算法参数;索引矩阵;判别分析;直觉模糊集;模型准确性;变异率;参数识别;生物系统建模;关联;模糊集;遗传算法;指标;数学模型;优化;

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专利

1. Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems [J] . E.Osaba, R.Carballedo, F.Diaz, ScientificWorldJournal . 2014,第3期

机译：交叉与突变：对应用于组合优化问题的遗传算法进化策略的比较分析
2. Fuzzy adaptation of crossover and mutation rates in genetic algorithms based on population performance [J] . Vannucci Marco, Colla Valentina Journal of intelligent & fuzzy systems: Applications in Engineering and Technology . 2015,第4期

机译：基于种群性能的遗传算法中交叉变异率的模糊自适应
3. Adapting Crossover and Mutation Rates in Genetic Algorithms [J] . WEN-YANG LIN, WEN-YUAN LEE, TZUNG-PEI HONG Journal of information science and engineering . 2003,第5期

机译：在遗传算法中适应交叉和变异率
4. InterCriteria Analysis of crossover and mutation rates relations in simple genetic algorithm [C] . Angelova Maria, Roeva Olympia, Pencheva Tania Federated Conference on Computer Science and Information Systems . 2015

机译：简单遗传算法交叉与突变率关系的相互统治度分析
5. Selective crossover as an adaptive strategy for genetic algorithms. [D] . Vekaria, Kanta Premji. 2000

机译：选择性交叉作为遗传算法的一种自适应策略。
6. Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems [O] . E. Osaba, R. Carballedo, F. Diaz, -1

机译：交叉与变异：遗传算法在组合优化问题中进化策略的比较分析
7. Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems [O] . E. Osaba, R. Carballedo, F. Diaz, 2014

机译：交叉与突变：对应用于组合优化问题的遗传算法进化策略的比较分析
8. Feasibility and Genetic Algorithms: The Behaviour of Crossover and Mutation [R] . Reid, D. J. 2000

机译：可行性和遗传算法：交叉和变异的行为

InterCriteria Analysis of crossover and mutation rates relations in simple genetic algorithm

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