• Publications
  • Influence
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
  • K. Deb, Himanshu Jain
  • Mathematics, Computer Science
  • IEEE Transactions on Evolutionary Computation
  • 1 August 2014
TLDR
We propose a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NS GA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. Expand
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Scalable Test Problems for Evolutionary Multiobjective Optimization
TLDR
In this study, we have suggested three different approaches for systematically designing test problems for this purpose. Expand
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Optimization for Engineering Design: Algorithms and Examples
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Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization
TLDR
In optimization studies including multi-objective optimization, the main focus is usually placed in finding the global optimum or global Pareto-optimal frontier, representing the best possible objective values. Expand
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Multiobjective Optimization
TLDR
Interactive Multiobjective Optimization Using a Set of Additive Value Functions and Dominance-Based Rough Set Approach. Expand
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Scalable test problems for evolutionary multi-objective optimization
TLDR
We propose three different approaches for systematically designing test problems for this purpose. Expand
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Real-coded evolutionary algorithms with parent-centric recombination
TLDR
We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-focused recombination operators (UNDX and SPX). Expand
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Implicit Niching in a Learning Classifier System: Nature's Way
TLDR
We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. Expand
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An evolutionary algorithm for constrained multi-objective optimization
TLDR
The paper follows the line of the design and evaluation of new evolutionary algorithms for constrained multi-objective optimization with a constraint handling technique and a diversity mechanism to obtain multiple nondominated solutions through the simple run of the algorithm. Expand
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Evolutionary algorithm for bilevel optimization using approximations of the lower level optimal solution mapping
TLDR
We introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. Expand
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