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    Article

    CHIP: Constraint Handling with Individual Penalty approach using a hybrid evolutionary algorithm

    Constraint normalization ensures consistency in scaling for each constraint in an optimization problem. Most constraint handling studies only address the issue to deal with constraints and use problem informat...

    Rituparna Datta, Kalyanmoy Deb, Jong-Hwan Kim in Neural Computing and Applications (2019)

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    Chapter and Conference Paper

    Constrained Engineering Design Optimization Using a Hybrid Bi-objective Evolutionary-Classical Methodology

    Constrained engineering design optimization problems are usually computationally expensive due to non-linearity and non convexity of the constraint functions. Penalty function methods are found to be quite pop...

    Rituparna Datta in Simulated Evolution and Learning (2010)

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    Chapter and Conference Paper

    A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization

    In this paper, we propose a hybrid reference-point based evolutionary multi-objective optimization (EMO) algorithm coupled with the classical SQP procedure for solving constrained single-objective optimization...

    Kalyanmoy Deb, Swanand Lele, Rituparna Datta in Advances in Computation and Intelligence (2007)