Multi-objective optimization using genetic algorithms: A tutorial

By Abdullah Konak, David W. Coit, Alice E. Smith. 2006

5,469 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Weight Agnostic Neural Networks (2019, methodology): “ith a minimal description length [35, 97, 98]. Given two different networks with similar performance we prefer the simpler network. By formulating the search as a multi-objective optimization problem [57, 83] we take into account the size of the network as well as its performance when ranking it in ”
Automating Biomedical Data Science Through Tree-Based Pipeline Optimization (2016, methodology): “In future work, we will explore methods such as multi-objective [19] and Pareto optimization [20], where one of the objectives/Pareto fronts can be pipeline generalization performance or pipeline complexity (assuming less-complex pipelines will generalize better).”
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, methodology): “The differences with respect to the values obtained by the MOEAs became larger as the risk-free return rate parameter increased. d) Skolpadungket et al. [33]: In this paper, six MOEAs were compared on the classical mean-variance model, including exact cardinality constraints, round-lot constraints, ”
Exergetic Optimization of Shell-and-Tube Heat Exchangers Using NSGA-II (2012, methodology): “The first multi-objective genetic algorithm (GA), called vector-evaluated GA (or VEGA), was proposed by Schaffer [24].”
A combined NLP‐differential evolution algorithm approach for the optimization of looped water distribution systems (2011, methodology): “A natural extension of this method to find an approximate Pareto front for multiobjective problems (to seed a full multiobjective combinatorial search) could be achieved by incorporating one of the many approaches to map multiobjective problems to a series of single objective problems [Konak et al.,”
A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments (2014, methodology): “Initially, Schaffer (1985) applied a genetic algorithm (GA) to solve a multi-objective problem and proposed a vector-evaluated GA.”
A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling (2015, background): “Since Schaffer put forward the vector evaluated genetic alg orithm [2] in 1984, a large number of multiobjective evolutio nary algorithms (MOEAs) have been proposed.”
Global Optimization Algorithms -- Theory and Application (2009, methodology): “The very first multi-objective genetic algorithm is the Vector Evaluated Genetic Algorithm (VEGA) created by Schaffer [497; 498] in the mid-1980s.”
Efficient deterministic multiple objective optimal control of (bio)chemical processes (2009, methodology): “, genetic algorithms (Srinivas and Deb, 1995; Deb et al., 2002; Konak et al., 2006) and particle swarm optimisation (Coello et al.”
Small Towers Make Big Differences (2020, background): “, 2010), exponential weighted sum (Yu, 1974; Athan and Papalambros, 1996), population methods (Schaffer, 1985), preference elicitation (Conitzer, 2009), among many more (Kochenderfer and Wheeler, 2019).”