User profiles for Kalyanmoy Deb
 | Kalyanmoy DebKoenig Endowed Chair Professor, Electrical and Computer Engineering, Michigan State … Verified email at egr.msu.edu Cited by 201661 |
A fast and elitist multiobjective genetic algorithm: NSGA-II
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing
have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M …
have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M …
Multi-objective optimisation using evolutionary algorithms: an introduction
K Deb - Multi-objective evolutionary optimisation for product …, 2011 - Springer
As the name suggests, multi-objective optimisation involves optimising a number of objectives
simultaneously. The problem becomes challenging when the objectives are of conflicting …
simultaneously. The problem becomes challenging when the objectives are of conflicting …
[BOOK][B] Optimization for engineering design: Algorithms and examples
K Deb - 2012 - books.google.com
This well-received book, now in its second edition, continues to provide a number of optimization
algorithms which are commonly used in computer-aided engineering design. The book …
algorithms which are commonly used in computer-aided engineering design. The book …
Comparison of multiobjective evolutionary algorithms: Empirical results
In this paper, we provide a systematic comparison of various evolutionary approaches to
multiobjective optimization using six carefully chosen test functions. Each test function involves …
multiobjective optimization using six carefully chosen test functions. Each test function involves …
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N Srinivas, K Deb - Evolutionary computation, 1994 - ieeexplore.ieee.org
In trying to solve multiobjective optimization problems, many traditional methods scalarize
the objective vector into a single objective. In those cases, the obtained solution is highly …
the objective vector into a single objective. In those cases, the obtained solution is highly …
Pymoo: Multi-objective optimization in python
Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …
related to data science, machine learning, and deep learning. Since optimization is an …
A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II
Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have
been mainly criticized for their (i) O(MN 3 ) computational complexity (where M is the number …
been mainly criticized for their (i) O(MN 3 ) computational complexity (where M is the number …
An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints
Having developed multiobjective optimization algorithms using evolutionary optimization
methods and demonstrated their niche on various practical problems involving mostly two and …
methods and demonstrated their niche on various practical problems involving mostly two and …
An efficient constraint handling method for genetic algorithms
K Deb - Computer methods in applied mechanics and …, 2000 - Elsevier
Many real-world search and optimization problems involve inequality and/or equality constraints
and are thus posed as constrained optimization problems. In trying to solve constrained …
and are thus posed as constrained optimization problems. In trying to solve constrained …
An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an …
In the precursor paper, a many-objective optimization method (NSGA-III), based on the NSGA-II
framework, was suggested and applied to a number of unconstrained test and practical …
framework, was suggested and applied to a number of unconstrained test and practical …