Multi-objective optimization using evolutionary algorithms

By Kalyanmoy Deb. 2001

14,626 citations recorded by the source corpus.

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Citations in the corpus

Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution (2018, methodology): “Tan et al. (2018) use a weighted product method (Deb & Kalyanmoy, 2001) to obtain a scalarized objective.”
Real-Time Federated Evolutionary Neural Architecture Search (2020, background): “By contrast, the Pareto approach to solving multi-objective optimization problems has been very popular and successful in evolutionary computation [43], which has also been extended to machine learning [44].”
Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “This concept will be used to reengineer the NLDT*, a discussion regarding which is provided in the next section.”
Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks (2017, background): “Lastly, changes in connection weights, biases, and moduls all involve polynomial mutation [86].”
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation (2009, neutral): “The search procedure presented here has already uncovered promising visual representations, however, it represents just the simplest first step one might take in conducting a large-scale search. For the sake of minimizing conceptual complexity, and maximizing the diversity of models analyzed, we cho”
Multi-Objective Optimization of a Row of Film Cooling Holes Using an Evolutionary Algorithm and Surrogate Modeling (2013, methodology): “[25] for two objective functions based on film cooling effectiveness and aerodynamic loss.”
A fuzzy definition of "optimality" for many-criteria optimization problems (2004, methodology): “When looking at both classical multiobjective optimization and at the 20 years of EMO history, it can be easily concluded that almost all algorithms have aimed at converging toward either a particular Pareto-optimal (PO) solution (classical methods) or toward the entire PO front, which is the set of”
Pareto Multi-Task Learning (2019, methodology): “Population-based and gradientfree multi-objective evolutionary algorithms [20, 21] are popular methods to find a set of welldistributed Pareto solutions in a single run.”
A Field Guide to Genetic Programming (2008, unknown): “See Chapter 9 and also (Deb, 2001).”
A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, methodology): “3) Genetic Operators: The simulated binary crossover [45] and polynomial mutation [46] are used to generate offsprings in all the considered MOEAs.”
NSGA and SPEA Applied to Multiobjective Design of Power Distribution Systems (2006, background): “SPEA has shown very good performance in different applications [13], [14].”
Efficient deterministic multiple objective optimal control of (bio)chemical processes (2009, background): “The research on scalar MOO problems has attracted much attention over the years (see, e.g., the books by Miettinen (1999) and Deb (2001)).”
A real coded genetic algorithm for solving integer and mixed integer optimization problems (2009, unknown): “Real coded genetic algorithms (RCGAs), which use real numbers for encoding, have fast convergence towards optima than binary and gray coded GAs ([22]).”
Quality Assessment of Pareto Set Approximations (2008, background): “In many application domains, it is useful to approximate the set of Paretooptimal solutions, cf. (Ehrgott and Gandibleux, 2000; Deb, 2001; Coello Coello et al., 2002).”
A distribution network expansion planning model considering distributed generation options and techo-economical issues (2010, methodology): “The Pareto optimality [18], is used to provide a pseudo fitness value for solution n, i.”