A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Free lunches on the discrete Lipschitz class
Theoretical Computer Science
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
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Algorithms for parameter optimization display subthreshold-seeking behavior when the majority of the points that the algorithm samples have an evaluation less than some target threshold. We first analyze a simple "subthreshold-seeker" algorithm. Further theoretical analysis details conditions that allow subthreshold-seeking behavior for local search algorithms using Binary and Gray code representations. The analysis also shows that subthreshold-seeking behavior can be increased by using higher bit precision. However, higher precision also can reduce exploration. A simple modification to a bit-climber is proposed that improves its subthreshold-seeking behavior. Experiments show that this modification results in both improved search efficiency and effectiveness on common benchmark problems.