Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems

404 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.

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms (2014, methodology): “Our MOEA is based on the Strength Pareto Evolutionary Algorithm v.2 (SPEA2) which has been considered as an " a posteriori " approach to multiobjective optimization (Zitzler and Thiele 1999; Zitzler et al. 2001).”
Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection (2008, methodology): “The SBX and polynomial mutation has been adopted in many MOEA papers (Zitzler et al., 2002; Deb et al., 2002a; Deb and Jain, 2002; Khare et al., 2003; Igel et al., 2007).”
An effective use of crowding distance in multiobjective particle swarm optimization (2005, background): “These EAs are population-based algorithms which allow them to explore the different parts of the Pareto front simultaneously.”