Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions describing the number of matching bits with a fixed target .
The Paper Story
Self-adjusting Asymmetric Mutation
- Evolutionary Algorithms (EAs) are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable.
- On bit strings, it might be known beforehand that zero-bits have a different interpretation than one-bits and that the total number of one-bits in high-quality solutions should lie within a certain interval.
- In the minimum spanning tree (MST) problem with vertices and edges, all valid solutions should have exactly one-bits.
In Self-adjusting Asymmetric Mutation, the mutation operator flips - and -bits of the current search point with different probabilities that can be adjusted based on the number of successes with a given probability profile.
On , we proved the optimization time , a speed-up by a factor of at least compared to the previous asymmetric from .
On general , where both contains zeroes and ones, we proved the same asymptotic bound.
Standard and Asymmetric Mutations
- Standard bit mutation: Flip each bit independently with probability . The Algorithm 1 with this mutation is called .
- Asymmetric mutation: Flip each -bit with probability for , where is defined as the number of -bits. The Algorithm 1 with the latter mutation is called [1,2].
We propose a rather conservative setting that flips -bits with probability for some value and -bits with probability .
- Initially, .
- In an observation phase of length the following pairs of - and -strengths are tried alternatingly:
- The pair that leads to relatively more successes is used as the ground pair for the next phase. In case of a tie a uniform random choice is made.
- The strengths are confined to .
- Fitness function .
- outperforms the from  by a constant factor of roughly 2 on .
Theorem: The runtime of the on satisfies where , , for an arbitrarily small constant , and .
Experimentally, was compared against the and  on in Figure 1, with parameters and . A clear difference between the performance of and the algorithms using asymmetric mutations. The outperforms although we used relatively small and large .
- Fitness function for an unknown target , where denotes the Hamming distance.
- In other words, returns the number of matching bits in and .
- does not harm its optimization time on :
Theorem: The runtime of the on is where , , and .
Experimentally, was compared against the and  on with in Table 2 with parameters: and . Similarity between data suggests that the asymmetric algorithms perform neither worse nor better compared to . More precisely, all p-values obtained from a Mann-Whitney U test between algorithms, with respect to the null hypothesis of identical behavior, are greater than 0.1.
 Jansen, T., Sudholt, D.: Analysis of an asymmetric mutation operator. Evolutionary Computation18(1), 1–26 (2010)
 Neumann, F., Wegener, I.: Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theoretical Computer Science378(1), 32–40 (2007)
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