TY - GEN
T1 - ACO beats EA on a dynamic pseudo-boolean function
AU - Kötzing, Timo
AU - Molter, Hendrik
PY - 2012/9/24
Y1 - 2012/9/24
N2 - In this paper, we contribute to the understanding of the behavior of bio-inspired algorithms when tracking the optimum of a dynamically changing fitness function over time. In particular, we are interested in the difference between a simple evolutionary algorithm (EA) and a simple ant colony optimization (ACO) system on deterministically changing fitness functions, which we call dynamic fitness patterns. Of course, the algorithms have no prior knowledge about the patterns. We construct a bit string optimization problem where we can show that the ACO system is able to follow the optimum while the EA gets lost.
AB - In this paper, we contribute to the understanding of the behavior of bio-inspired algorithms when tracking the optimum of a dynamically changing fitness function over time. In particular, we are interested in the difference between a simple evolutionary algorithm (EA) and a simple ant colony optimization (ACO) system on deterministically changing fitness functions, which we call dynamic fitness patterns. Of course, the algorithms have no prior knowledge about the patterns. We construct a bit string optimization problem where we can show that the ACO system is able to follow the optimum while the EA gets lost.
UR - https://www.scopus.com/pages/publications/84866356355
U2 - 10.1007/978-3-642-32937-1_12
DO - 10.1007/978-3-642-32937-1_12
M3 - Conference contribution
AN - SCOPUS:84866356355
SN - 9783642329364
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 122
BT - Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
T2 - 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012
Y2 - 1 September 2012 through 5 September 2012
ER -