23 citations · 64 across the 7 of their papers we have counts for
7 papers · 1 filter
An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems
Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman +1
Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as inter-variable relationships to assist…
It is Time for New Perspectives on How to Fight Bloat in GP
Francisco Fernández de Vega, Gustavo Olague, Francisco Chávez +3
The present and future of evolutionary algorithms depends on the proper use of modern parallel and distributed computing infrastructures. Although still sequential approaches domin…
Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems
Zhun Fan, Wenji Li, Zhaojun Wang +6
This paper proposes a push and pull search method in the framework of differential evolution (PPS-DE) to solve constrained single-objective optimization problems (CSOPs). More spec…
MOEA/D with Angle-based Constrained Dominance Principle for Constrained Multi-objective Optimization Problems
Zhun Fan, Yi Fang, Wenji Li +3
This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance principle (ACDP) embedded in a decomposition-based multi-objective evolutionary al…
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems
Zhun Fan, Wenji Li, Xinye Cai +5
This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the…
An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
Zhun Fan, Wenji Li, Xinye Cai +6
This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrain…