Feature Selection using Simulated Annealing with Optimal Neighborhood Approach

Improving the Neighborhood Selection Strategy in Simulated Annealing using the Optimal Stopping Problem

Simulated Annealing is one the most well known local search methods. In practice, it is often used to solve discrete optimization problems; especially very tough problems, [54, 37, 34, 3, 2, 41]. Global optimization is computationally extremely challenging and for large instances, exact methods reach their limitations quickly. Hence, in practice, often local optimization methods are used. Simul...

Neighborhood Parallel Simulated Annealing

Simulated Annealing with Advanced Adaptive Neighborhood

It was Kirkpatrick et al. who first proposed simulated annealing, SA, as a method for solving combinatorial optimization problems[1]. It is reported that SA is very useful for several types of combinatorial optimization problems[2]. The advantages and the disadvantages of SA are well summarized in [3]. The most remarkable disadvantages are that it needs a lot of time to find the optimum solutio...

Temperature Parallel Simulated Annealing with Adaptive Neighborhood

In this paper, a Temperature Parallel Simulated Annealing with Adaptive Neighborhood (TPSA/AN) for continuous optimization problems is introduced. TPSA/AN is based on the temperature parallel simulated annealing (TPSA), which is suitable for parallel processing, and the SA that Corana developed for continuous optimization problems. The moves in TPSA/AN are adjusted to have equal acceptance rate...

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification...