TY - GEN
T1 - Quasi-bistability of walk-based landscape measures in stochastic fitness landscapes
AU - Werth, Bernhard
AU - Pitzer, Erik
AU - Ostermayer, Gerald
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Exploratory landscape analysis is a useful method for algorithm selection, parametrization and creating an understanding of how a heuristic optimization algorithm performs on a problem and why. A prominent family of fitness landscape analysis measures are based on random walks through the search space. However, most of these features were only introduced on deterministic fitness functions and it is unclear, under which conditions walk-based landscape features are applicable to noisy optimization problems. In this paper, we empirically analyze the effects of noise in the fitness function on these measures and identify two dominant regimes, where either the underlying problem or the noise are described. Additionally, we observe how step sizes and walk lengths of random walks influence this behavior.
AB - Exploratory landscape analysis is a useful method for algorithm selection, parametrization and creating an understanding of how a heuristic optimization algorithm performs on a problem and why. A prominent family of fitness landscape analysis measures are based on random walks through the search space. However, most of these features were only introduced on deterministic fitness functions and it is unclear, under which conditions walk-based landscape features are applicable to noisy optimization problems. In this paper, we empirically analyze the effects of noise in the fitness function on these measures and identify two dominant regimes, where either the underlying problem or the noise are described. Additionally, we observe how step sizes and walk lengths of random walks influence this behavior.
KW - Fitness landscape analysis
KW - Heuristic optimization
KW - Noisy optimization
UR - http://www.scopus.com/inward/record.url?scp=85050627550&partnerID=8YFLogxK
U2 - 10.1145/3205455.3205471
DO - 10.1145/3205455.3205471
M3 - Conference contribution
T3 - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
SP - 1087
EP - 1094
BT - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PB - Association for Computing Machinery, Inc
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -