Performance Comparison Of Evolutionary Algorithms For Image Clustering [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications ...
P. Civicioglu +5 more
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A Multi-Objective Planning Framework for Optimal Integration of Distributed Generations [PDF]
, 2013 This paper presents an evolutionary algorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO2 emissions,
Howe, Joe +2 more
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Absolutely free extrinsic evolution of passive low-pass filter [PDF]
, 2006 Evolutionary electronics is a brunch of evolvable hardware, where the evolutionary algorithm is applied towards electronic circuits. The success of evolutionary search most of all depends on variable length representation methodology. The low-pass filter
Kalganova, T, Sapargaliyev, Y
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Evolutionary-based sparse regression for the experimental identification of duffing oscillator [PDF]
, 2020 In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data.
Crevecoeur, Guillaume +4 more
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Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation
IEEE Transactions on Evolutionary Computation, 2020 By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties.
Jian-Yu Li +4 more
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IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems [PDF]
IEEE Transactions on Evolutionary Computation, 2018 Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based
Y. Sun, G. Yen, Zhang Yi
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Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context [PDF]
, 2010 A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue.
Baron, Claude +3 more
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Analysis of some global optimization algorithms for space trajectory design [PDF]
, 2010 In this paper, we analyze the performance of some global search algorithms on a number of space trajectory design problems. A rigorous testing procedure is introduced to measure the ability of an algorithm to identify the set of ²-optimal solutions. From
Di Lizia P. +9 more
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Evolutionary algorithms and dynamic programming [PDF]
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 2009 Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion.
Doerr, B. +4 more
openaire +4 more sources
An Experimental Study on Competitive Coevolution of MLP Classifiers
Mendel, 2017 This paper investigates the effectiveness and efficiency of two competitive (predator-prey) evolutionary procedures for training multi-layer perceptron classifiers: Co-Adaptive Neural Network Training, and a modified version of Co-Evolutionary Neural ...
Marco Castellani, Rahul Lalchandani
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