[PDF] Top 20 Hyperspectral classification optimization using multiobjective evolutionary algorithm

... All spectral signatures used in the current research were collected in a controlled environment. Further research on classification based on spectra from in-situ underwater measurement is required in the context ... See full document

... genetic algorithm involves a population of individuals that are represented as chromosomes, each consisting of a set of genes that describe a ...in evolutionary algorithms is available in McClymont et ... See full document

... continuous multiobjective optimization problem with 𝑚 objective functions is a piecewise continuous (𝑚 − 1)-dimensional manifold in the decision space under some mild ...design multiobjective ... See full document

... earliest multiobjective optimization in enhancing the production of succinic acid is carried out by ...Pareto Evolutionary Algorithm 2 (SPEA2) and Non-Dominated Sorting Genetic ... See full document

... Next, we present the process of efficient frontier formu- lation by quoting experimental results based on historical data of FTSE-100. For the first experiment, as a training set we use 63 daily historical observations ... See full document

... Multiobjective optimization (MO) is the problem of maximizing/minimizing a set of non- linear objective functions (modeling several performance criteria) subject to a set of nonlinear constraints (modeling ... See full document

... Multi-objective optimization has also been increasingly employed in chemical ...generic algorithm (MOGA) was used to optimize the design of the pressure swing adsorption ...multi-objective ... See full document

... hand, evolutionary methods (evolutionary algorithms EAs) for MOPs optimize all objectives si- multaneously and generate a set of alternative ...simultaneous optimization can fit with population based ... See full document

... or evolutionary multiobjective optimization algorithm is used to pre-compute a well-spread Pareto set; for a recent review on evolutionary-based interactive and a posteriori decision ... See full document

... Genetic Algorithm II (NSGA-II): This algorithm is known as Non- dominated Sorting Genetic Algorithm II (NSGA-II) is introduced by Deb and Agarwal in [22] as an improved version of the NSGA ... See full document

... an evolutionary algorithm, both of which are known to need many function ...extensive optimization procedures. In the multiobjective world, the cost is multiplied, because there are multiple ... See full document

... foraging optimization algorithm (ABFOA) for solving multiobjective flexible job-shop scheduling problem ...foraging optimization algorithm (BFOA) is a new bio-inspired ... See full document

... EA-based multiobjective optimization procedure that builds upon the Tchebycheff ...solved using mathematical programming procedures to estimate the non-inferior so- lutions for multiobjective ... See full document

... A multiobjective optimization problem is an optimization problem with more than one objective functions under a set of feasibility ...conditions. Multiobjective optimization problems ... See full document

... by using evolutionary ...by using nonlinear ...performed using standard steepest descent or evolutionary computing ...performed using a FLANN ... See full document

... The experimental results for the tested algorithms are pre- sented in Fig. 5 and Table I. In Table I, the mean results are reported for each algorithm. In the table, for each algo- rithm the IGD values are ... See full document

... highly-efficient multiobjective optimization technique based on MOEA/D-GO (multiobjective evolutionary algorithm based on decomposition combined with enhanced genetic operators) for the ... See full document

... HSI classification in the last ...HSI Classification optimization problem [6] [7] [8] ...the classification performance, different kinds of SVM-based al- gorithms [11]-[19] have been ... See full document

... In architectures for cognitive radios, adaptive DPD systems that operate under Pareto-optimized configurations are highly desirable due to the multidimensional space of relevant implementation metrics. However, prior ... See full document

... different multiobjective feature selection ...the multiobjective feature selection procedure applied on all the S × L × E × 2 fea- tures corresponding to the possible segments, levels, variances of the ... See full document