作业帮谁能把英语速度相关表示法归纳下
来源：学生作业帮 编辑：作业帮 分类：英语作业 时间：2024/11/09 02:07:32
谁能把英语速度相关表示法归纳下
Second, two biological modelling intelligence optimization algorithm unification frame pattern [7] the biological modelling intelligence optimization algorithm in aspects and so on structure, research content and method and movement pattern manifested the big similarity, has provided the possibility for the establishment biological modelling intelligence optimization algorithm's unified frame pattern.
forms the community of the individual, rests on the specific evolution rule, the iteration produces the renewal community (for example genetic algorithm, ant group algorithm) or the individual position (for example grain of subgroup algorithm, artificial school of fish algorithm, mix leapfrog algorithm), the optimal solution evolves unceasingly along with the community or the migration appears suddenly, this frame pattern may describe is:
1) establishes various parameters, produces the initial community and calculates the adaptation value;
2) acts according to the hypothesis rule, the renewal community or its position, has group of solutions, the computation individual adaptation value;
3) obtains the community by the individual adaptation value comparison the optimal-adaptive value and makes the record;
4) judges the terminal condition whether to satisfy, if satisfies, conclusion iteration; Otherwise, transfers 2).
in this frame pattern, the one who decides the algorithm performance is community's renewal rule, these hypothesis rule had decided the individual behavior standards, have the direct biology foundation, constituted the algorithm to be different with other similar unique essences and the bright characteristic.
the biological modelling intelligence optimization algorithm sets up together the call-board generally, with records the most superior individual the historical condition. In algorithm execution each iteration, each individual comparison own condition and call-board condition, and when own condition is superior with it replacement, causes the call-board to record the historical most superior condition throughout. After algorithm iteration conclusion, may read out the optimal solution from the call-board condition and gain the related information
forms the community of the individual, rests on the specific evolution rule, the iteration produces the renewal community (for example genetic algorithm, ant group algorithm) or the individual position (for example grain of subgroup algorithm, artificial school of fish algorithm, mix leapfrog algorithm), the optimal solution evolves unceasingly along with the community or the migration appears suddenly, this frame pattern may describe is:
1) establishes various parameters, produces the initial community and calculates the adaptation value;
2) acts according to the hypothesis rule, the renewal community or its position, has group of solutions, the computation individual adaptation value;
3) obtains the community by the individual adaptation value comparison the optimal-adaptive value and makes the record;
4) judges the terminal condition whether to satisfy, if satisfies, conclusion iteration; Otherwise, transfers 2).
in this frame pattern, the one who decides the algorithm performance is community's renewal rule, these hypothesis rule had decided the individual behavior standards, have the direct biology foundation, constituted the algorithm to be different with other similar unique essences and the bright characteristic.
the biological modelling intelligence optimization algorithm sets up together the call-board generally, with records the most superior individual the historical condition. In algorithm execution each iteration, each individual comparison own condition and call-board condition, and when own condition is superior with it replacement, causes the call-board to record the historical most superior condition throughout. After algorithm iteration conclusion, may read out the optimal solution from the call-board condition and gain the related information