genetic adaptation 中文意思是什麼

genetic adaptation 解釋
遺傳適應性
  • genetic : adj. 1. 遺傳（學）上的。2. 發生的，發展的；創始的。adv. -ically
  • adaptation : n. 1. 適合，適應，順應 （to）。2. 改編（的作品） （from）。3. 【生物學】適應性的改變；感官適應性調節。4. 同化。
  1. It is necessary for a species " adaptation and evolutionary potential to keep genetic variety at a relative level. genetic diversity in the population must not be lost furthermore and gene exchange among individuals should be enhanced as much as possible to avoid extinction of crested ibis

    一定程度的遺傳變異性是維持種群的適應力與進化潛力的必要條件，為使朱?免於滅絕的厄運，必須避免其種群遺傳多樣性的進一步喪失，並盡可能增加個體間的基因交流，最大限度地避免近親繁殖。
  2. It can get a relative satisfied result using self - adaptation and study of genetic algorithms ( ga ), and can get a lot of them due to ga ' s parallelism. therefore we can choose a better one according to the reality

    由於遺傳演算法的并行性，可以獲得多個滿意解，因而可以根據實際情況，比較選擇，具有較好的可替代性，有一定的經濟實用價值。
  3. Researchers are looking into many aspects of nystagmus, including possible drug treatments, its epidemiology, impact on visual function, adaptation of the visual system to the constant eye movements, the causes of the condition and its genetic make - up

    研究者們調查了眼球震顫癥的很多方面，包括可能的藥物治療，流行病學，對視覺功能的影響，及視覺系統如何適應眼球的不斷運動，該病的病因及遺傳學特性等等。
  4. In the second charper, two reformed metheds has presented, which are competition adaptation - ga based on elitist and dual mutation adaptive - ga which can increase the population diversity and can decrease the dependence of genetic algorithms " result to the control parameter of operators and the status of initial population. to testify their abilities of algorithms, some studies have excuted which included the study to optimize parameter for pid and the study to multivariable intelligent decoupling control for mimo system. the outcomes have showed us that this amendment has a better effective than conventional means and the genetic algorithms which have no change

    第二章中，在已有文獻的基礎上，創新性的提出兩種改進演算法：基於最優保留的聯賽競爭機制遺傳演算法（ competitionadaptation - gabasedonelitist ）和雙變異自適應遺傳演算法（ dualmutationadaptive - ga ） ；通過對演算法中選擇運算元和變異運算元的有效改進，提高了演算法的尋優能力和尋優效率，增加了群體中個體模式的多樣性，對于演算法中存在的欺騙問題、早熟問題以及成熟前收斂問題有明顯的改進作用。
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