By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This e-book and its significant other quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth foreign convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers offered have been conscientiously reviewed and chosen from 198 submissions. The papers are geared up in 18 cohesive sections, three unique periods and one aggressive consultation protecting all significant subject matters of swarm intelligence examine and improvement comparable to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; man made bee colony algorithms; man made immune approach; evolutionary algorithms; neural networks and fuzzy equipment; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; type equipment; GPU-based tools; scheduling and direction making plans; instant sensor networks; strength process optimization; swarm intelligence in photograph and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
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Additional resources for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II
16]. F A good collection of methods and applications can be found in the books by Mellin and Castillo , and Bunke and Kendel [11, 12]. 3 Methodology Pattern Recognition and Machine Learning field have established research work on the combination of multiple classifiers (also known as ensemble of classifiers, Mixture of experts). Overall predictive accuracy can be increased by the use of multiple classifiers instead of a single classifier. The ensemble procedure constitute two steps mainly module formation and then integration of results of modules.
Further the number of biases is equal to the number of neurons. The total number of biases is H + O. This means that for a single layer ANN there would be I x H + H x O + H + O parameters to be optimized. The fitness of any individual in the population is measured with the help of fitness function. The fitness function consists of the ANN along with its training data set. In the fitness function we initialize the ANN by the various parameters that are generated by GA. These parameters were extracted from the individual and used to set the weights and biases of the ANN.
It is quite likely that BPA results in some local minima in place of global minima. Also we need to specify the initial parameters before the learning starts. These pose restrictions on the use of ANNs. The GA on the other hand is known for its ability of optimization. In this section we will fuse this capability of the GA along with the ANNs to train the ANN. This solution overcomes much of the problems with the ANN training. The block diagram of the proposed system for breast cancer diagnosis is shown in Figure 1.
Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II by Ying Tan, Yuhui Shi, Carlos A Coello Coello