On Using Populations of Sets in Multiobjective Optimization

Authors:
Johannes Bader;Dimo Brockhoff;Samuel Welten;Eckart Zitzler
Affiliations:
Computer Engineering and Networks Lab, ETH Zurich, Zurich, Switzerland 8092;Computer Engineering and Networks Lab, ETH Zurich, Zurich, Switzerland 8092;Computer Engineering and Networks Lab, ETH Zurich, Zurich, Switzerland 8092;Computer Engineering and Networks Lab, ETH Zurich, Zurich, Switzerland 8092
Venue:
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Year:
2009

Citing 10
Cited 4

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
A Parallel Genetic Algorithm for Multiobjective Microprocessor Design

Proceedings of the 6th International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Using the Multi-Start and Island Models for Parallel Multi-Objective Optimization on the Computational Grid

E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Covariance Matrix Adaptation for Multi-objective Optimization

Evolutionary Computation
SPAM: Set Preference Algorithm for Multiobjective Optimization

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parallel Approaches for Multiobjective Optimization

Multiobjective Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit

IEEE Transactions on Evolutionary Computation

Dynamical multi-objective optimization using evolutionary algorithm for engineering

ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A new multi-objective evolutionary algorithm based on a performance assessment indicator

Proceedings of the 14th annual conference on Genetic and evolutionary computation
GECCO 2012 tutorial on evolutionary multiobjective optimization

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial on evolutionary multiobjective optimization

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.