Titlebook: Computational Intelligence and Intelligent Systems; 7th International Sy Kangshun Li,Jin Li,Aniello Castiglione Conference proceedings 2016

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An Optimized Clustering Algorithm Using Improved Gene Expression Programmingpresentation according to extended traditional gene expression programming used in GEP-ADF. It is aimed at improving the performance of GEP to obtain center points more accurately. Experimental results show that our new algorithm has good performance in clustering and the three real world datasets compared with the other two algorithms.

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退潮

Custodian

Stoßprobleme in Physik, Technik und Medizincts such as slow convergence speed and poor accuracy of solution. In order to improve the performance of GSO in solving complex optimization problems, an opposition-based learning approach (OBL) and a differential evolution method (DE) are integrated into GSO to form a hybrid GSO. In this paper, the

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https://doi.org/10.1007/978-3-642-13117-2s. In the standard FA and its most variants, a firefly moves to other brighter fireflies. If the current firefly is brighter than another one, the current one will not be conducted any search. In this paper, we propose a new firefly algorithm (called NFA) to address this issue. In NFA, brighter fire

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透明

https://doi.org/10.1007/978-3-642-95245-6slow convergent speed. To be aim at these problems, a improved genetic algorithm which is based on the immune mechanism was proposed in this paper. In this new algorithm, the immune mechanism was used to ensure the diversity of population. Meanwhile, a improved character encoding was adopted to furt

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Sedative

Miscellaneous Strabismus Syndromes,mmunication, has been widely applied in military and civil fields. From the perspective of resource- saving, under the condition of the network’s connectivity and specific coverage, the number of sensor nodes is assumed to be opened as few as possible. So, computing the sensor nodes collection which

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Siddharth Agrawal,Ankur Yadav,Neha Singhined optimization problem (COP) into an equivalent dynamic constrained many-objective optimization problem (DCMOP), then present many-objective optimization evolutionary algorithm with dynamic constraint handling mechanism, called MaDC, to solve the DCMOP, thus the COP is addressed. MaDC uses DE as