Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
About:
The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
Citations
More filters
Proceedings ArticleDOI
A dominance-based stability measure for multi-objective evolutionary algorithms
TL;DR: This paper introduces what it calls a “stability measure” and uses this measure to estimate when to stop the multi-objective evolutionary search.
Proceedings ArticleDOI
Challenges for evolutionary multiobjective optimization algorithms in solving variable-length problems
Hui Li,Kalyanmoy Deb +1 more
TL;DR: The preliminary experimental results show that MOEA/D-M2M shows good potential in solving the multiobjective test problems with variable-length structures due to its diversity strategy along different search directions, and correlation analysis on the Pareto solutions with variable sizes in thePareto front indicates that mating restriction is necessary in solvingVariable-length problem.
Journal ArticleDOI
Disaster Rescue Task Scheduling: An Evolutionary Multiobjective Optimization Approach
TL;DR: In this paper, a fuzzy multiobjective optimization problem of rescue task scheduling is proposed to simultaneously maximize the task scheduling efficiency and minimize the operation risk for the rescue team, and an efficient multi-objective biogeography-based optimization (EMOBBO) algorithm is developed to solve the problem.
Proceedings ArticleDOI
Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track
TL;DR: This paper provides the first known empirical results on the robustness of MOGP solutions in an unseen environment consisting of real-world financial data and focuses on two well-known mechanisms to determine which leads to the more robust solutions: Mating Restriction, and Diversity Preservation.
Proceedings ArticleDOI
A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems
TL;DR: This contribution presents a multi-objective evolutionary algorithm to obtain linguistic models with improved accuracy and the least number of possible rules.