Christian Gießen

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The (1+1) EA with mutation probability <i>c/n</i>, where <i>c</i>&#62;0 is an arbitrary constant, is studied for the classical OneMax function. Its expected optimization time is analyzed exactly (up to lower order terms) as a function of <i>c</i> and &#955;. It turns out that 1/<i>n</i> is the only optimal mutation probability if &#955;=<i>o</i>(ln <i>n</i>(More)
We consider stochastic versions of OneMax and LeadingOnes and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend these results to LeadingOnes and to many different noise(More)
BACKGROUND The AIO KRK-0104 randomised phase II trial investigated the efficacy and safety of two capecitabine-based regimens: combination of capecitabine and irinotecan (CAPIRI) plus cetuximab (CAPIRI-C) and combination of capecitabine with oxaliplatin (CAPOX) plus cetuximab (CAPOX-C) in the first-line treatment of metastatic colorectal cancer (mCRC).(More)
BACKGROUND Liver-limited disease (LLD) denotes a specific subgroup of metastatic colorectal cancer (mCRC) patients. PATIENTS AND METHODS A total of 479 patients with unresectable mCRC from an irinotecan-based randomised phase III trial were evaluated. Patients with LLD and non-LLD and hepatic resection were differentiated. Based on baseline patient(More)
The ( $$1+\lambda $$ 1 + λ ) EA with mutation probability c / n, where $$c>0$$ c > 0 is an arbitrary constant, is studied for the classical OneMax function. Its expected optimization time is analyzed exactly (up to lower order terms) as a function of c and $$\lambda $$ λ . It turns out that 1 / n is the only optimal mutation probability if $$\lambda =o(\ln(More)
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the(More)
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