Proves sharp threshold on mutation parameter χ for (1+1)-EA on Dynamic Binary Value and Uniform weight dynamic linear problems, yielding O(n log n) runtime below threshold and 2^Ω(n) above, plus a second stagnation-distance threshold for the former.
Swarm and Evolutionary Computation6, 1–24 (2012)
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes a scalable benchmark for DMOPs with changing objective counts by dynamically selecting subsets from fixed Minus-DTLZ and Minus-WFG problems to isolate the effect of objective number dynamics.
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The $(1 + 1)$-EA in Dynamic Environments
Proves sharp threshold on mutation parameter χ for (1+1)-EA on Dynamic Binary Value and Uniform weight dynamic linear problems, yielding O(n log n) runtime below threshold and 2^Ω(n) above, plus a second stagnation-distance threshold for the former.
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A Scalable Benchmark Test Suite for Dynamic Multi-Objective Optimization with a Changing Number of Objectives
Proposes a scalable benchmark for DMOPs with changing objective counts by dynamically selecting subsets from fixed Minus-DTLZ and Minus-WFG problems to isolate the effect of objective number dynamics.