📄 genetic_algorithm.htm
字号:
style="MARGIN-LEFT: 84pt; TEXT-INDENT: -36pt; tab-stops: list 84.0pt; mso-list: l24 level2 lfo24"><![if !supportLists]><SPAN
lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体; mso-bidi-font-family: 宋体"><SPAN
style="mso-list: Ignore">3．3<SPAN
style="FONT: 7pt 'Times New Roman'">
</SPAN></SPAN></SPAN><![endif]><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">测试<SPAN lang=EN-US><SPAN
style="mso-spacerun: yes">
</SPAN></SPAN>－<SPAN lang=EN-US>11</SPAN>－<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal
style="MARGIN-LEFT: 48pt; TEXT-INDENT: -24pt; tab-stops: list 48.0pt; mso-list: l23 level1 lfo21"><![if !supportLists]><B
style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体; mso-bidi-font-family: 宋体"><SPAN
style="mso-list: Ignore">四、<SPAN
style="FONT: 7pt 'Times New Roman'">
</SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">结束语<SPAN lang=EN-US><SPAN
style="mso-spacerun: yes">
</SPAN><SPAN style="mso-spacerun: yes"> </SPAN><SPAN
style="mso-spacerun: yes"> </SPAN><SPAN
style="mso-spacerun: yes"> </SPAN></SPAN></SPAN></B><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">－<SPAN lang=EN-US>15</SPAN>－<B
style="mso-bidi-font-weight: normal"><SPAN
lang=EN-US><o:p></o:p></SPAN></B></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 24.1pt; mso-char-indent-count: 2.0"><B
style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">附录</SPAN></B><SPAN lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><SPAN
style="mso-spacerun: yes">
</SPAN></SPAN><SPAN style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">－<SPAN
lang=EN-US>16</SPAN>－<B style="mso-bidi-font-weight: normal"><SPAN
lang=EN-US><o:p></o:p></SPAN></B></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 24pt; mso-char-indent-count: 2.0"><SPAN
lang=EN-US style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 24pt; mso-char-indent-count: 2.0"><SPAN
lang=EN-US style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 24pt; mso-char-indent-count: 2.0"><SPAN
lang=EN-US style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 24pt; mso-char-indent-count: 2.0"><SPAN
lang=EN-US style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></P>
<P class=MsoNormal
style="MARGIN-LEFT: 21.75pt; TEXT-INDENT: -21.75pt; TEXT-ALIGN: center; tab-stops: list 21.75pt; mso-list: l25 level1 lfo4"
align=center><![if !supportLists]><B style="mso-bidi-font-weight: normal"><SPAN
lang=EN-US
style="FONT-SIZE: 16pt; FONT-FAMILY: 宋体; mso-bidi-font-family: 宋体"><SPAN
style="mso-list: Ignore">一.<SPAN
style="FONT: 7pt 'Times New Roman'">
</SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 16pt; FONT-FAMILY: 宋体">进化算法理论<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></B></P>
<P class=MsoNormal><B style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></B></P>
<P class=MsoNormal
style="MARGIN-LEFT: 36pt; TEXT-INDENT: -36pt; tab-stops: list 36.0pt; mso-list: l11 level2 lfo5"><![if !supportLists]><B
style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 15pt; mso-fareast-font-family: 'Times New Roman'"><SPAN
style="mso-list: Ignore">1．1<SPAN
style="FONT: 7pt 'Times New Roman'"> </SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 15pt; FONT-FAMILY: 宋体">进化算法概述<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></B></P>
<P class=MsoNormal
style="TEXT-INDENT: 24pt; LINE-HEIGHT: 20pt; mso-char-indent-count: 2.0; mso-line-height-rule: exactly"><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">从远古时代单细胞开始，历经环境变迁的磨难，生命经历从低级到高级，从简单到复杂的演化历程。生命不断地繁衍生息，产生出具有思维和智能的高级生命体。人类得到生命的最佳结构与形式，它不仅可以被动地适应环境，更重要的是它能够通过学习，模仿与创造，不断提高自己适应环境的能力。<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal
style="TEXT-INDENT: 24pt; LINE-HEIGHT: 20pt; mso-char-indent-count: 2.0; mso-line-height-rule: exactly"><SPAN
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体">进化算法就是借鉴生物自然选择和遗传机制的随机搜索算法。进化算法通过模拟“优胜劣汰，适者生存”的规律激励好的结构，通过模拟孟德尔的遗传变异理论在迭代过程中保持已有的结构，同时寻找更好的结构。作为随机优化与搜索算法，进化算法具有如下特点：进化算法不是盲目式的乱搜索，也不是穷举式的全面搜索，它根据个体生存环境即目标函数来进行有指导的搜索。进化算法只需利用目标的取值信息而不需要其他信息，因而适用于大规模、高度非线性的不连续、多峰函数的优化，具有很强的通用性；算法的操作对象是一组个体，而非单个个体，具有多条搜索轨迹。<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal
style="TEXT-INDENT: 24pt; LINE-HEIGHT: 20pt; mso-char-indent-count: 2.0; mso-line-height-rule: exactly"><SPAN
lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体; mso-font-kerning: 0pt"><o:p> </o:p></SPAN></P>
<P class=MsoNormal
style="MARGIN-LEFT: 36pt; TEXT-INDENT: -36pt; tab-stops: list 36.0pt; mso-list: l11 level2 lfo5"><![if !supportLists]><B
style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 14pt; mso-fareast-font-family: 'Times New Roman'"><SPAN
style="mso-list: Ignore">1．2<SPAN
style="FONT: 7pt 'Times New Roman'"> </SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 14pt; FONT-FAMILY: 宋体">遗传算法<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></B></P>
<P class=MsoNormal style="TEXT-INDENT: 29.25pt; LINE-HEIGHT: 150%"><SPAN
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体">遗传算法（<SPAN
lang=EN-US>Genetic A</SPAN></SPAN><SPAN lang=EN-US
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体; mso-font-kerning: 0pt; mso-bidi-font-family: Tahoma">lgorithm</SPAN><SPAN
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体">）是进化算法的一个重要分支。它由<SPAN
lang=EN-US>John
Holland</SPAN>提出，最初用于研究自然系统的适应过程和设计具有自适应性能的软件。近来，遗传算法作为问题求解和最优化的有效工具，已被非常成功地应用与解决许多最优化问题并越来越流行。<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 29.25pt; LINE-HEIGHT: 150%"><SPAN
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体">遗传算法的主要特点是群体搜索策略和群体中个体之间的信息互换<SPAN
lang=EN-US>,</SPAN>它实际上是模拟由个体组成的群体的整体学习过程<SPAN
lang=EN-US>,</SPAN>其中每个个体表示问题搜索空间中的一个解点<SPAN
lang=EN-US>.</SPAN>遗传算法从任一初始的群体出发<SPAN lang=EN-US>,</SPAN>通过随机选择，交叉和变异等遗传操作<SPAN
lang=EN-US>,</SPAN>使群体一代代地进化到搜索空间中越来越好的区域<SPAN lang=EN-US>,</SPAN>直至抵达最优解点<SPAN
lang=EN-US>.<o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 29.25pt; LINE-HEIGHT: 150%"><SPAN
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体">遗传算法和其它的搜索方法相比<SPAN
lang=EN-US>,</SPAN>其优越性主要表现在以下几个方面：首先<SPAN
lang=EN-US>,</SPAN>遗传算法在搜索过程中不易陷入局部最优<SPAN
lang=EN-US>,</SPAN>即使在所定义的适应度函数非连续<SPAN
lang=EN-US>.</SPAN>不规则也能以极大的概率找到全局最优解<SPAN lang=EN-US>,</SPAN>其次<SPAN
lang=EN-US>,</SPAN>由于遗传算法固有的并行性<SPAN lang=EN-US>,</SPAN>使得它非常适合于大规模并行分布处理<SPAN
lang=EN-US>,</SPAN>此外<SPAN lang=EN-US>,</SPAN>遗传算法易于和别的技术<SPAN
lang=EN-US>(</SPAN>如神经网络<SPAN lang=EN-US>.</SPAN>模糊推理<SPAN
lang=EN-US>.</SPAN>混沌行为和人工生命等<SPAN lang=EN-US>)</SPAN>相结合<SPAN
lang=EN-US>,</SPAN>形成性能更优的问题求解方法<SPAN lang=EN-US>.<o:p></o:p></SPAN></SPAN></P>
<P class=MsoNormal style="TEXT-INDENT: 29.25pt; LINE-HEIGHT: 150%"><SPAN
lang=EN-US
style="FONT-SIZE: 12pt; LINE-HEIGHT: 150%; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></P>
<P class=MsoNormal
style="MARGIN-LEFT: 21.75pt; TEXT-INDENT: -21.75pt; TEXT-ALIGN: center; tab-stops: list 21.75pt; mso-list: l25 level1 lfo4"
align=center><![if !supportLists]><B style="mso-bidi-font-weight: normal"><SPAN
lang=EN-US
style="FONT-SIZE: 16pt; FONT-FAMILY: 宋体; mso-bidi-font-family: 宋体"><SPAN
style="mso-list: Ignore">二.<SPAN
style="FONT: 7pt 'Times New Roman'">
</SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 16pt; FONT-FAMILY: 宋体">遗传算法<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></B></P>
<P class=MsoNormal><B style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 14pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></B></P>
<P class=MsoNormal
style="MARGIN-LEFT: 42pt; TEXT-INDENT: -42pt; tab-stops: list 42.0pt; mso-list: l14 level2 lfo26"><![if !supportLists]><B
style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 14pt; FONT-FAMILY: 宋体; mso-bidi-font-family: 宋体"><SPAN
style="mso-list: Ignore">2．1<SPAN
style="FONT: 7pt 'Times New Roman'">
</SPAN></SPAN></SPAN></B><![endif]><B style="mso-bidi-font-weight: normal"><SPAN
style="FONT-SIZE: 14pt; FONT-FAMILY: 宋体">遗传算法的基本流程<SPAN
lang=EN-US><o:p></o:p></SPAN></SPAN></B></P>
<P class=MsoNormal><B style="mso-bidi-font-weight: normal"><SPAN lang=EN-US
style="FONT-SIZE: 12pt; FONT-FAMILY: 宋体"><o:p> </o:p></SPAN></B></P>
<P
style="WORD-BREAK: break-all; TEXT-INDENT: 27pt; mso-char-indent-count: 2.25">一个串行运算的遗传算法通常按如下过程进行：
</P>
<P
style="WORD-BREAK: break-all; TEXT-INDENT: 30pt; mso-char-indent-count: 2.5"><SPAN
lang=EN-US>(1) </SPAN>对待解决问题进行<U>编码</U>；<SPAN lang=EN-US>t:=0 </SPAN></P>
<P
style="WORD-BREAK: break-all; TEXT-INDENT: 30pt; mso-char-indent-count: 2.5"><SPAN
lang=EN-US>(2) </SPAN><U>随机初始化</U>群体<SPAN lang=EN-US>X(0):=<SUB><!--[if gte vml 1]><v:shapetype id=_x0000_t75 coordsize =
"21600,21600" o:preferrelative = "t" o:spt = "75" filled = "f" stroked = "f"
path = " m@4@5 l@4@11@9@11@9@5 xe"> <v:stroke joinstyle =
"miter"></v:stroke><v:formulas><v:f eqn =
"if lineDrawn pixelLineWidth 0 "></v:f><v:f eqn = "sum @0 1 0 "></v:f><v:f eqn =
"sum 0 0 @1 "></v:f><v:f eqn = "prod @2 1 2 "></v:f><v:f eqn =
"prod @3 21600 pixelWidth "></v:f><v:f eqn =
"prod @3 21600 pixelHeight "></v:f><v:f eqn = "sum @0 0 1 "></v:f><v:f eqn =
"prod @6 1 2 "></v:f><v:f eqn = "prod @7 21600 pixelWidth "></v:f><v:f eqn =
"sum @8 21600 0 "></v:f><v:f eqn = "prod @7 21600 pixelHeight "></v:f><v:f eqn =
"sum @10 21600 0 "></v:f></v:formulas><v:path o:extrusionok = "f"
gradientshapeok = "t" o:connecttype = "rect"></v:path><o:lock aspectratio="t"
v:ext="edit"></o:lock></v:shapetype><v:shape id=_x0000_i1025
style="WIDTH: 69pt; HEIGHT: 18pt" o:ole="" type = "#_x0000_t75" coordsize =
"21600,21600"><v:imagedata o:title="" src =
"yichuan.files/image001.wmz"></v:imagedata></v:shape><![endif]--><![if !vml]><img width=92 height=24src="yichuan.files/image002.gif" v:shapes="_x0000_i1025"><![endif]></SUB><!--[if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.3" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1254726188"> </o:OLEObject></xml><![endif]--></SPAN>；<SPAN
lang=EN-US> </SPAN></P>
<P
style="MARGIN-LEFT: 56.95pt; WORD-BREAK: break-all; TEXT-INDENT: -24pt; mso-char-indent-count: -2.0; mso-para-margin-left: 3.14gd"><SPAN
lang=EN-US>(3) </SPAN>对当前群体<SPAN lang=EN-US>X(t)</SPAN>中每个<U>染色体</U><SPAN
lang=EN-US><SUB><!--[if gte vml 1]><v:shape id=_x0000_i1026
style="WIDTH: 12pt; HEIGHT: 18pt" o:ole="" type = "#_x0000_t75" coordsize =
"21600,21600"> <v:imagedata o:title="" src =
"yichuan.files/image003.wmz"></v:imagedata></v:shape><![endif]--><![if !vml]><img width=16 height=24src="yichuan.files/image004.gif" v:shapes="_x0000_i1026"><![endif]></SUB><!--[if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.3" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1254726190"> </o:OLEObject></xml><![endif]--></SPAN>计算其<U>适应度</U><SPAN
lang=EN-US>F <SUB><!--[if gte vml 1]><v:shape id=_x0000_i1027
style="WIDTH: 21pt; HEIGHT: 18pt" o:ole="" type = "#_x0000_t75" coordsize =
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -