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On the Analysis of Dynamic Restart Strategies for Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
INFORMS Journal on Computing
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minimising the delta test for variable selection in regression problems
International Journal of High Performance Systems Architecture
The curse of dimensionality in data mining and time series prediction
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Using an adaptive memory strategy to improve a multistart heuristic for sequencing by hybridization
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
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Proper selection of variables is necessary when dealing with large number of input dimensions in regression problems. In the paper, we investigate the behaviour of landscape that is formed when using Delta test as the optimization criterion. We show that simple and greedy Forward-backward selection procedure with multiple restarts gives optimal results for data sets with large number of samples. An improvement to multistart Forward-backward selection is presented that uses information from previous iterations in the form of long-term memory.