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1、-Evolutionary Principles applied to Problem Solving遗传算法There is nothing particularly new about Evolutionary Solvers or Genetic Algorithms. The first references to this field of putation stem from the early 60s when Lawrence J. Fogel published the landmark paper On the Organization of Intellect which
2、 sparked the first endeavours into evolutionary puting. The early 70s witnessed further forays with seminal work produced by -among others- Ingo Rechenberg and John Henry Holland. Evolutionary putation didnt gain popularity beyond the programmer world until Richard Dawkins book The Blind Watchmaker
3、in 1986, which came with a small program that generated a seemingly endless stream of body-plans called Bio-morphs based on human selection. Since the 80s the advent of the personal puter has made it possible for individuals without government funding to apply evolutionary principles to personal pro
4、jects and they have since made it into the mon parlance.其实在遗传算法和基因算法里并什么特别新的理论出现，该领域的第一篇文献出现在六十年代由Lawrence J. Fogel 出版的具有里程碑意义的论文“智能组织，这篇论文使人们开场致力于研究遗传算法。七十年代又由ngo Rechenberg and John Henry Holland的工作进一步带动了基因算法的开展，遗传算法直到1986年才因为Richard Dawkins的“The Blind Watchmake而让人广为人知，里面有个小的例子，基于人类的选择仍然会产生无尽的被称为“
5、生态形变的动作方案。80年代由于个人电脑的出现使得每个人都可以将进化算法用于个人工程而不用政府提供资金支持，从此，进化算法开场像日常话题一样进入公众视野。The term Evolutionary puting may very well be widely known at this point in time, but they are still very much a programmers tool. By programmers for programmers if you will. The applications out there that apply evolutiona
6、ry logic are either aimed at solving specific problems, or they are generic libraries that allow otherprogrammers to piggyback along. It is my hope that Galapagos will provide a generic platform for the application of Evolutionary Algorithms to be used on a wide variety of problems by non-programmer
7、s.虽然现在进化算法在现在已经广为人知，但他大局部还作为编程工具使用，只是程序员和程序员之间交流，遗传算法即使应用也是处理一些特别的问题，大局部时间还是呆在通用库里等着程序员来用它，我希望galapagos可以提供一个遗传算法的广泛平台来让那些非编程人员来处理一些问题。Pros and Cons赞成和反对Before we dive into the subject matter too deeply though I feel it is important to highlight some of the (dis)advantages of this particular type of
8、 solver, just so you know what to e*pect. Since we are not living in the best of all possible worlds there is often no such thing as the perfect solution. Every approach has drawbacks and limitations. In the case of Evolutionary Algorithms these are luckily well known and easily understood drawbacks
9、, even though they are not trivial. Indeed, they may well be prohibitive for many a particular problem.在我们深入探讨这个问题之前，我认为有必要向大家介绍这类特殊算法处理方式的优缺点，这样可能更利于你了解你到底想通过这个获得什么。事实上我们生活在一个不完美的世界，没有什么事情会有一个完美地解决方案，每个解决方案都会有他的缺陷和限制。而在遗传算法领域可喜的是我们明确地知道他的一些缺陷，而且这些缺陷也不小，而且确实在一些具体问题的处理上明确制止用遗传算法来解决。Firstly; Evolution
10、ary Algorithms are slow. Dead slow. It is not unheard of that a single process may run for days or even weeks. Especially plicated set-ups that require a long time in order to solve a single iteration will quickly run out of hand. A light/shadow or acoustic putation for e*ample may easily take a min
11、ute per iteration. If we assume well need at least 50 generations of 50 individuals each (which is almost certainly an underestimate unless the problem has a very obvious solution.) were already looking at a two-day runtime.首先，遗传算法在运行起来的时候很慢，奇慢，非常慢，慢的要死。一个小程序运行几天甚至几个星期一点也不见怪，尤其是需要长时间简单迭代的复杂数据构造，运行起来
12、会马上超出你的掌控，就算简单的光影分析或者是声学分析每次迭代就要几分钟，假设我们设置50代，每代50个个体这都是按照最少最理想的状态来设定的，那这个程序需要两天才能做完。Secondly, Evolutionary Algorithms do not guarantee a solution. Unless a predefined good-enough value is specified, the process will tend to run on indefinitely, never reaching The Answer, or, having reached it, not
13、recognizing it for what it is.其次，遗传算法并不能百分之百的保证求出正确的解，除非一个预先你已经想好的差不多的解已经确定下来，遗传算法会尽量的朝着你想要的那个解努力，但是他永远不会找到“真理，当然，也可能找到了只不过他是电脑，根本意识不到那个是最优解，即“真理。All is not bleak and dismal however, Evolutionary Algorithms have strong benefits as well, some of them rather unique amongst the plethora of putational m
14、ethods. They are remarkably fle*ible for e*ample, able to tackle a wide variety of problems. There are classes of problems which are by definition beyond the reach of even the best solver implementation and other classes that are very difficult to solve, but these are typically rare in the province
15、of the human meso-world. By and large the problems we encounter on a daily basis fall into the evolutionary solvable category.当然，遗传算法也并不是上面我说的则单调无力，一直被吐槽。进化算法在一些方面也有着强大的性能表达，在茫茫多的计算算法里他确实一枝独秀，进化算法有很强的灵活性，能同时处理各种各样的问题，有些问题最好的解算器也解不出来，而且很多的我们日常遇到的问题都可以放到进化算法的畴进展解决。Evolutionary Algorithms are also quit
16、e forgiving. They will happily chew on problems that have been under- or over-constrained or otherwise poorly formulated. Furthermore, because the run-time process is progressive, intermediate answers can be harvested at practically any time. Unlike many dedicated algorithms, Evolutionary Solvers spew forth a never ending stream of answers, where newer answers are generally of a higher quality than older answers. So even a pre-maturel
