A Micro-Genetic Algorithm for Multiobjective Optimization

By Carlos Artemio Coello Coello, Gregorio Toscano Pulido. International Conference on Evolutionary Multi-Criterion Optimization, 2001

470 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/3556f462d060b73499873ac9c30c7b6526770de9

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Citations in the corpus

Multi-objective optimization using genetic algorithms: A tutorial (2006, methodology): “Afterwards, several multi-objective evolutionary algorithms were developed including Multi-objective Genetic Algorithm (MOGA) [6], Niched Pareto Genetic Algorithm (NPGA) [7], Weight-based Genetic Algorithm (WBGA) [8], Random Weighted Genetic Algorithm (RWGA)[9], Nondominated Sorting Genetic Algorith”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, methodology): “of some early methods has also been overcome by a gradual progression with the introduction of more efficient algorithms such as niched Pareto genetic algorithm (NPGA) [30] and Pareto archived evolution strategy (PAES) [40], which first attempted to use reduce the cost of niching, through to the mic”
A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition (2017, methodology): “Furthermore, Coello and Pulido [11] proposed a multiobjective mGA with two memories, i.e., population memory and external memory.”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, methodology): “Some of the most popular algorithms, according to the state-of-the-art review of Coello Coello (2005), are the Strength Pareto Evolutionary Algorithm (SPEA; Zitzler & Thiele, 1999) and its successor SPEA II (Zitzler et al.”
A review on genetic algorithm: past, present, and future (2020, background): “Coello and Pulido [28] developed a multiobjective micro GA.”
Handling multiple objectives with particle swarm optimization (2004, methodology): “3) Microgenetic Algorithm for Multiobjective Optimization: This approach was introduced by Coello Coello and Toscano Pulido [3], [6].”
A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018, methodology): “The Multiobjective Micro GA. Coello and Pulido (2001) is an algorithm that uses a very small population size in conjunction with an archive.”
Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes (2006, methodology): “The proposed genetic algorithm is based on the micro-GA, developed by Coello and Toscano (2001).”