Selection algorithm

Abstract: Most implementations of genetic algorithms experience sampling bias and are unnecessarily inefficient. This paper reviews various sampling algorithms proposed in the literature and offers two new algorithms of reduced bias and increased efficiency. An empirical analysis of bias is then presented.

Abstract: A feature subset selection algorithm based on branch and bound techniques is developed to select the best subset of m features from an n-feature set. Existing procedures for feature subset selection, such as sequential selection and dynamic programming, do not guarantee optimality of the selected feature subset. Exhaustive search, on the other hand, is generally computationally unfeasible. The present algorithm is very efficient and it selects the best subset without exhaustive search. Computational aspects of the algorithm are discussed. Results of several experiments demonstrate the very substantial computational savings realized. For example, the best 12-feature set from a 24-feature set was selected with the computational effort of evaluating only 6000 subsets. Exhaustive search would require the evaluation of 2 704 156 subsets.

Abstract: This paper focuses on reducing the power consumption of wireless microsensor networks. Therefore, a communication protocol named LEACH (low-energy adaptive clustering hierarchy) is modified. We extend LEACH's stochastic cluster-head selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime by about 30% can be accomplished. Furthermore, we present a new approach to define lifetime of microsensor networks using three new metrics FND (First Node Dies), HNA (Half of the Nodes Alive), and LND (Last Node Dies).

Abstract: We introduce a new multiobjective evolutionary algorithm called PESA (the Pareto Envelope-based Selection Algorithm), in which selection and diversity maintenance are controlled via a simple hyper-grid based scheme. PESA's selection method is relatively unusual in comparison with current well known multiobjective evolutionary algorithms, which tend to use counts based on the degree to which solutions dominate others in the population. The diversity maintenance method is similar to that used by certain other methods. The main attraction of PESA is the integration of selection and diversity maintenance, whereby essentially the same technique is used for both tasks. The resulting algorithm is simple to describe, with full pseudocode provided here and real code available from the authors. We compare PESA with two recent strong-performing MOEAs on some multiobjective test problems recently proposed by Deb. We find that PESA emerges as the best method overall on these problems.

Abstract: In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.