论文标题
频率健身分配：在目标函数值的两种二主转换下使优化算法不变
Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value
论文作者
论文摘要
在频率适应性分配（FFA）下，与目标值相对应的适应性是其在适应性分配步骤中的遇到频率，并且会受到最小化。 FFA使优化过程在目标函数价值的两种射位转换下不变。在twomax，跳跃和陷阱函数上，具有1/s标准突变的经典（1+1）-EA可以在s中具有预期的runtimens指数。在我们的实验中，A（1+1）-FEA，相同的算法，但使用FFA，表现出的平均运行时间似乎扩展为$ s^2 \ ln {s} $。由于跳跃和陷阱是Onemax的肉体转变，因此在这三个方面的行为相同。在OneMax，Leaders和Plateau问题上，它似乎比S Linear in Sinear in s中的（1+1）-EA慢。 （1+1）-FEA的性能要比W-Model和MaxSAT实例上的（1+1）-EA好得多。我们通过将MD5校验和计算作为上述某些问题转换并产生相同的行为来进一步验证两次不变性。最后，我们表明FFA可以提高为车间安排的模因算法的性能。
Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in fitness assignment steps and is subject to minimization. FFA renders optimization processes invariant under bijective transformations of the objective function value. On TwoMax, Jump, and Trap functions of dimension s, the classical (1+1)-EA with standard mutation at rate 1/s can have expected runtimes exponential in s. In our experiments, a (1+1)-FEA, the same algorithm but using FFA, exhibits mean runtimes that seem to scale as $s^2\ln{s}$. Since Jump and Trap are bijective transformations of OneMax, it behaves identical on all three. On OneMax, LeadingOnes, and Plateau problems, it seems to be slower than the (1+1)-EA by a factor linear in s. The (1+1)-FEA performs much better than the (1+1)-EA on W-Model and MaxSat instances. We further verify the bijection invariance by applying the Md5 checksum computation as transformation to some of the above problems and yield the same behaviors. Finally, we show that FFA can improve the performance of a memetic algorithm for job shop scheduling.
