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Multiplicative drift analysis
Drift analysis is one of the strongest tools in the analysis of evolutionary algorithms. Its main weakness is that it is often very hard to find a good drift function. In this paper, we make progressExpand
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From black-box complexity to designing new genetic algorithms
Black-box complexity theory recently produced several surprisingly fast black-box optimization algorithms. In this work, we exhibit one possible reason: These black-box algorithms often profit fromExpand
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Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings
While evolutionary algorithms are known to be very successful for a broad range of applications, the algorithm designer is often left with many algorithmic choices, for example, the size of theExpand
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  • Open Access
Drift analysis and linear functions revisited
We regard the classical problem how the (1+1) Evolutionary Algorithm optimizes an arbitrary linear pseudo-Boolean function. We show that any such function is optimized in time (1 + o(1)) 1.39en lnExpand
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Multiplicative Drift Analysis
We introduce multiplicative drift analysis as a suitable way to analyze the runtime of randomized search heuristics such as evolutionary algorithms. Our multiplicative version of the classical driftExpand
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  • Open Access
Mutation Rate Matters Even When Optimizing Monotonic Functions
Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotonic. TheseExpand
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k-Bit Mutation with Self-Adjusting k Outperforms Standard Bit Mutation
When using the classic standard bit mutation operator, parent and offspring differ in a random number of bits, distributed according to a binomial law. This has the advantage that all HammingExpand
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Calculation of Discrepancy Measures and Applications
In this book chapter we survey known approaches and algorithms to compute discrepancy measures of point sets. After providing an introduction which puts the calculation of discrepancy measures in aExpand
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  • Open Access
IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics
IOHprofiler is a new tool for analyzing and comparing iterative optimization heuristics. Given as input algorithms and problems written in C or Python, it provides as output a statistical evaluationExpand
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  • Open Access
Unknown solution length problems with no asymptotically optimal run time
We revisit the problem of optimizing a fitness function of unknown dimension; that is, we face a function defined over bit-strings of large length N, but only n ≪ N of them have an influence on theExpand
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