Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach

By Iason Hatzakis, David R. Wallace. Annual Conference on Genetic and Evolutionary Computation, 2006

341 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling (2007, background): “After the idea is put forward earlier [5], there has been a lukewarm interest on this topic [7, 6].”
Surrogate-assisted evolutionary computation: Recent advances and future challenges (2011, background): “In such strategies, a surrogate can be helpful in learning the changing fitness function [72–74].”
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization (2009, background): “One approach is to extend the idea of offline error applied in dynamic SO optimization and calculate the time averages of static metrics [ 23 ].”
Evolutionary dynamic optimization: A survey of the state of the art (2012, methodology): “along the idea of offline performance 427 [65, 62, 66, 67].”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “Other DMOEAs assume that a DMOP changes constantly [32], [33], [41]; thus, Line 4 is skipped.”
A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization (2017, background): “teristics, and studies along this direction can be referred to [22], [28], [32], [33], [36], [47], [54], and [55].”