By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This e-book and its significant other quantity, LNCS vol. 8794 and 8795 represent the lawsuits of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers awarded have been conscientiously reviewed and chosen from 198 submissions. The papers are prepared in 18 cohesive sections, three designated classes and one aggressive consultation overlaying all significant subject matters of swarm intelligence learn and improvement reminiscent of novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for vacationing salesman challenge; man made bee colony algorithms; synthetic immune approach; evolutionary algorithms; neural networks and fuzzy tools; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; type tools; GPU-based tools; scheduling and direction making plans; instant sensor networks; energy 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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Extra resources for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II
The next move is determined by a probabilistic transition rule that is a function of locally available pheromone trails. Once ant has constructed a solution, then it updates the pheromone trial values which depend on the quality of solutions constructed by the ants. Finally, the ant constructs the optimal solution with the higher amount of pheromone trails. The algorithm stops iterating when an end condition is satisfied. The search for the optimal feature subset is a traversal through the graph where a minimal number of nodes are visited and the end conditions are satisfied .
The main objective of feature selection is to find a minimal feature subset from a set of features with high performance in representing the original features . In classification problems, feature selection is a necessary step due to lots of irrelevant or redundancy features. By eliminating these features, the dimensionality of feature can be reduced and the predictive performance can be improved for classification. Feature selection methods are dimensionality reduction methods often associated to data mining tasks of classification , which provide a reduced subset of the original features while preserving the representative power of the original features.
Single point crossover was used. The program was executed till 100 generations. 65% for testing respectively. Here the results of GA are exported which are optimized weights and bias of ANN and ANN is run for 10,000 epochs. The result of this is passed through various integrators. We then experiment ensemble model with various integration methods to find an optimized parameter which gives best performance. After getting the optimized parameter, the detection procedure is run 20 times for the same configuration.
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