TY - GEN
T1 - On using populations of sets in multiobjective optimization
AU - Bader, Johannes
AU - Brockhoff, Dimo
AU - Welten, Samuel
AU - Zitzler, Eckart
PY - 2010/12/1
Y1 - 2010/12/1
N2 - Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a 'real' evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application.
AB - Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a 'real' evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application.
U2 - 10.1007/978-3-642-01020-0_15
DO - 10.1007/978-3-642-01020-0_15
M3 - Conference contribution
AN - SCOPUS:78650730568
SN - 3642010199
SN - 9783642010194
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 154
BT - Evolutionary Multi-Criterion Optimization - 5th International Conference, EMO 2009, Proceedings
T2 - 5th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2009
Y2 - 7 April 2009 through 10 April 2009
ER -