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題名 | 人工智慧技術--遺傳演算法=Artificial Intelligence Technique--Genetic Algorithms |
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作者 | 方曉嵐; | 書刊名 | 技術與訓練 |
卷期 | 23:2=189 1998.04[民87.04] |
頁次 | 頁149-155 |
專輯 | 電控專集 |
分類號 | 440.8 |
關鍵詞 | 人工智慧; 遺演算法; 一代; 染色體; 基因; 評估; 擇偶; 育種; 交配; 突變; 族群替代; 適度; 表示法; 初始化; 精英論; Arificial intelligence; AI; Genetic algorithms; GAs; Generation; Chromosome; Gene; Evaluation; Selection; Breeding; Crossover; Mutation; Population update; Fitness; Representation; Initialization; Elitism; |
語文 | 中文(Chinese) |
中文摘要 | 遺傳演算法是源自新達爾文進化論的靈感而發展出來的一套解決問題的演算法。在 遺傳演算法中對一個問題的候選解(稱為染色體)會被評估,然後新的候選解將會由選來配 對的父母染色體經過交配及突變而產生出來。此新的候選解即為繼承父母染色體部份特質的 子女染色體。而新一代的染色體會被評估並選來交配育種而產生更新的下一代。這個週期會 一直重覆直到找到一組滿意的解答或事先給定的進化時間終了為止。 |
英文摘要 | Genetic Algorithms (GAs) are a group of methods which solve problems using algorithms inspired by the processes of NeoDarwinian evolutionary theory. In A GA, the performance of a set of candidates to a problem (call "chromosomes") are evaluated, then new candidate solutions are produced by selecting candidates as "parents" and applying crossover and/or mutation operators which combine bits of two parents to produce one or more children. The new set of candidates is then evaluated, and this cycle continues until an adequate solution is found or the pre-set CPU time is up. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。