利用改進(jìn)遺傳算法求解方程組
摘要
選擇、交叉和變異是遺傳算法的幾個(gè)主要操作算子，它們構(gòu)成了遺傳操作。本文對(duì)遺傳操作提出了改進(jìn)方案,即對(duì)于交叉操作：如果兩個(gè)子代的適應(yīng)度均比父代大就交換，如果子代的適應(yīng)度1個(gè)比父代大而另1個(gè)比父代小則保留大的子代而還原小的子代為父代，如果子代的適應(yīng)度均比父代小則取消此次的交換。變異操作中對(duì)每個(gè)父代的`多個(gè)位置逐個(gè)變異,如果子代的適應(yīng)度比父代大則變異,否則不變異。通過(guò)解線性方程組和非線性方程組證明了該方法能夠使得遺傳始終向著理想的方向，避免了算法陷入死循環(huán)，并且收斂速度非�？�。
關(guān)鍵詞：遺傳算法；遺傳操作；解方程組；改進(jìn)遺傳算法，最優(yōu)化
Improvement genetic algorithms for solving equation group
Abstract
Choice, cross and variation are the main operators of the genetic algorithms, which constitute the so-called genetic operation. The paper give an improvement project of the genetic algorithms. That is :if both of the two children’s flexibility are smaller than their father’s in the choice operation, than cancel the choice; and in the genetic operation, several positions for each father are changed one by one ,if the children’ flexibility is bigger than his father’s, than variating ,otherwise does not happen. This kind of method has been proved that it can make the heredity always go in the perfect direction, the algorithms avoid sinking into dead circulation, and the convergence speed is very quick by using it in solving equations.
Keywords: genetic algorithms; genetic operation; solving equations; improvement genetic algorithms; optimization
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