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Optimizing Worst-Case Scenario in Evolutionary Solutions to the MasterMind Game
| Content Provider | Semantic Scholar |
|---|---|
| Author | Balamurali, A. Singh, Abhishek Singh, Tejesh Pratap |
| Copyright Year | 2019 |
| Abstract | The MasterMind puzzle is an interesting problem to be approached via evolutionary algorithms, since it is at the same time a constrained and a dynamic problem, and has eventually a single solution. In previous papers we have presented and evaluated different evolutionary algorithms to this game and shown how their behavior scales with size, looking mainly at the game-playing performance. In this paper we fine-tune the parameters of the evolutionary algorithms so that the worst case number of evaluations, and thus the average and median, are improved, resulting in a better solution in a more reliably predictable time. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijesc.org/upload/6154911a8615452b0b873b8e72251d4a.Optimizing%20Worst-case%20Scenario%20in%20Evolutionary%20Solutions%20to%20the%20MasterMind%20Game%20(4).pdf |
| Alternate Webpage(s) | http://ijesc.org/upload/0eaec29368ef667a6f0e22140f4fdfc7.Optimizing%20Worst-case%20Scenario%20in%20Evolutionary%20Solutions%20to%20the%20MasterMind%20Game%20(1).pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
