cs.NEFeb 27, 2026
SaveAll Mutation Rates for the Evolutionary Algorithm
Abstract
For every real number and for all , there is a fitness function for which the optimal mutation rate for the evolutionary algorithm on , denoted , satisfies in that . In other words, the set of all for which the mutation rate is optimal for the EA is dense in the interval . To show this, a fitness function is introduced which is called HillPathJump.
Figures & tables
| 0000 0000 0000, | 0000 0000 0001, | 0000 0000 0011, | 0000 0000 0111, |
| 0000 0000 1111, | 0000 0001 1111, | 0000 0011 1111, | 0000 0111 1111, |
| 0000 1111 1111, | 0000 1111 0111, | 0000 1111 0011, | 0000 1111 0001, |
| 0000 1111 0000, | 0001 1111 0000, | 0011 1111 0000, | 0111 1111 0000, |
| 1111 1111 0000, | 1111 1111 0001, | 1111 1111 0011, | 1111 1111 0111, |
| 1111 1111 1111, | 1111 0111 1111, | 1111 0011 1111, | 1111 0001 1111, |
| 1111 0000 1111, | 1111 0000 0111, | 1111 0000 0011, | 1111 0000 0001, |
Table 1: Expanded gray code. The left-most column is the gray code with each bit repeated 4 times. This defines a path from left to right, top to bottom.
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Evolutionary Optimization
Evolutionary optimization searches for strong solutions through population-based variation, selection, and adaptation.
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