or an even simpler implementation would be to ask whether t

基于程序创造关卡似乎能够有效地减少开发时间。但是每次当你去运行它时都要去创造一个能够生成理想结果的算法，而这与设计一组关卡一样需要消耗大量时间。通常大量开发时间都是用于评估生成结果并添加某些限制因素去确保所创造的内容具有可玩性。通过将这种生成内容验证法作为一种优化问题，我们便可以使用现有的算法去找出适合预期结果的解决方法。

前面几个关卡都是随机生成的。为了发展这些关卡，我们必须基于它们如何带给玩家满足感去评估它们（游戏邦注：也就是一个关卡的乐趣度）。然后我们可以使用这些评分去衡量这些关卡并尝试着将这些关卡的有效功能结合在一起以创造出一个更优秀的新关卡。

But how do we score these levels? How do we know which ones are good or bad? In most genetic algorithms, there is a fitness function that evaluates the generated solution. But it would be very hard to write a function that can interpret the generated levels and assign it a proper score.

生成内容

I want to use explicit player feedback, asking them what they think of the content generated. The levels in my game generally have a short play time. Every time a player finishes a level, they are asked what they think of the level they just played. For the first implementation I will use a 5-star rating system. There are, however, many different ways to ask the player for this feedback. Different approaches would be, for example, to ask the player to rank the levels they play from bad to good, or an even simpler implementation would be to ask whether the player liked the level or not.

The new generation

个人和全局选择

适应度函数