@inproceedings{1349, abstract = {Crossing fitness valleys is one of the major obstacles to function optimization. In this paper we investigate how the structure of the fitness valley, namely its depth d and length ℓ, influence the runtime of different strategies for crossing these valleys. We present a runtime comparison between the (1+1) EA and two non-elitist nature-inspired algorithms, Strong Selection Weak Mutation (SSWM) and the Metropolis algorithm. While the (1+1) EA has to jump across the valley to a point of higher fitness because it does not accept decreasing moves, the non-elitist algorithms may cross the valley by accepting worsening moves. We show that while the runtime of the (1+1) EA algorithm depends critically on the length of the valley, the runtimes of the non-elitist algorithms depend crucially only on the depth of the valley. In particular, the expected runtime of both SSWM and Metropolis is polynomial in ℓ and exponential in d while the (1+1) EA is efficient only for valleys of small length. Moreover, we show that both SSWM and Metropolis can also efficiently optimize a rugged function consisting of consecutive valleys.}, author = {Oliveto, Pietro and Paixao, Tiago and Heredia, Jorge and Sudholt, Dirk and Trubenova, Barbora}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference 2016 }, location = {Denver, CO, USA}, pages = {1163 -- 1170}, publisher = {ACM}, title = {{When non-elitism outperforms elitism for crossing fitness valleys}}, doi = {10.1145/2908812.2908909}, year = {2016}, }