Procedural generation strategies

May 3rd, 2016
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1. Procedural generation strategies
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4. Before deciding what algorithm to use, you need to remember that there probably isn't a perfect algorithm. There rarely is. So whatever you choose it will have advantages and disadvantages. You have to think in terms of trade-offs.
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7. Run-time;
8. Memory usage;
9. Quality of solution;
10. Variety of solutions;
11. Work time (To design+write your algorithm).
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14. [1] Generate a random solution.
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16. - Predictable fast run-time.
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20. [2] Generate random solutions over and over, until you find one "good enough" that passes some tolerance.
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22. - It will find a good solution, providing the search terminates.
23. - Unpredictable run-time. May not terminate at all if the tolerance is set too high.
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26. [3] Generate a fixed number of random solutions, and pick the best of those.
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28. - Predictable run-time. Could be fast or slow, depending on the number chosen.
29. - Not guaranteed to find a good solution every time. It might even be a terrible solution some times.
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32. [4] Generate a random solution, then iteratively modify it to get better solutions.
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34. - Should be fast.
35. - May find a "local maximum", whereby your modifications won't improve it, because the much better solutions are too different from what you ended up with.
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38. [5] Solve the problem on paper, and hand-pick solutions you like.
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40. - Very fast run-time, as the computer is just picking a random thing from a list.
41. - Potentially good solutions, because you've hand-picked them.
42. - Lacks variation, as the solutions are limited to the few you've chosen.
43. - Depending on the nature of the problem, it could use a lot of memory to store all these picked solution. It might be a lot of work.
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