Sa´nchez D, Melin P (2013) Multi-objective hierarchical genetic algorithm for modular neural network optimization using a granular approach. In: Castillo O, Melin P, Kacprzyk J (eds) Recent advances on hybrid Intelligent systems, vol 451. Springer, Berlin, Heidelberg, pp 107-120Sánchez, D...
Genetic algorithm is a single objective evolutionary computation algorithm that has been used for automatic clustering. Holland developed the algorithm in the early 1970s (Holland, 1975). Its idea stemmed from Charles Darwin’s principle of evolution by natural selection. In genetic algorithms, some ...
Robots that evolve on demand Article 12 September 2024 Morphological flexibility in robotic systems through physical polygon meshing Article 12 June 2023 Determining optimum assembly zone for modular reconfigurable robots using multi-objective genetic algorithm Article Open access 03 January 2025 Introdu...
The procedure was implemented on Optis, a software for hierarchical multi-objective calibration of LSEMs. Optis is based on the multi-objective genetic algorithm, Non-dominated Sorted Genetic Algorithm-II (NSGA-II). The calibration was hierarchically performed from the fastest process (radiative ...
Objective function of each algorithm with a centroid value of 6.43768 8 (a), and the probability of objective function of 11.90%, 30.95%, 50%, 69.04%, and 88.09% (b) for GWO, MGWO, MMSCC-GWO, CCS-GWO, and HMS-GWO respectively. ...
O. Schütze, A new data structure for the nondominance problem in multi-objective optimization, inInternational Conference on Evolutionary Multi-Criterion Optimization(Springer, 2003), pp. 509–518 X. Chen, Pareto tree searching genetic algorithm: approaching pareto optimal front by searching pareto op...
with an objective of directing clinical practice. The subtype and progression signatures presented here may help elucidate the molecular mechanism of invasion in both subtypes. More importantly, it will allow quantitative researchers to use these data as a benchmark for the development of suitable clas...
The main objective of this study is to explore the identification of customer heterogeneity and classification by using the Hierarchical Bayesian Model (HBM). Moreover, in order to resolve the efficiency issues regarding the reference estimates of the Hierarchical Bayesian model, we have integrated ...
wheremis the number of samples,σ^2is the estimated value of theσ2; andRRis the correlation matrix. The likelihood function is typically multimodal. Therefore, an evolutionary algorithm such as a genetic algorithm is typically adopted to solve the optimization problem shown in Eq. (4). However...
The objective function of ELD is to minimize the fuel cost of generating units during a specific period of operation. The dimensions of HSP, DED, LSTPP, and ELD are 96, 120, 126, and 140, respectively. All of them are large-dimensional problems. HMRFO and MRFO were each run 51 times...