The concept of fitness is introduced, and a simple derivation of the Fundamental Theorem of Natural Selection (which states that the average fitness of a population increases if its variance is nonzero) is given. After a short discussion of the adaptative walk model, a short review is given of the quasispecies approach to molecular evolution and to the error threshold. The relevance of flat fit...

Protein binding and function often involves conformational changes. Advanced nuclear magnetic resonance (NMR) experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may "select" protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transitio...

Input variable selection is a key preprocess step in any I/O modelling problem. Normally, better generalization performance is obtained when unneeded parameters coming from irrelevant or redundant variables are eliminated. Information theory provides a robust theoretical framework for performing input variable selection thanks to the concept of mutual information. Nevertheless, for continuous v...

the data of this study were collected by indigenous chicken breeding centers in the west azerbaijan for 11 generations. the (co) variance components, heritability's, correlations between traits and breeding values were estimated by multi-trait animal model using wombat software. the genetic trends of traits over 11 generations of selection, using average function of breeding values per gen...

We show algorithmic randomness versions of the two classical theorems on subsequences of normal numbers. One is Kamae-Weiss theorem (Kamae 1973) on normal numbers, which characterize the selection function that preserves normal numbers. Another one is the Steinhaus (1922) theorem on normal numbers, which characterize the normality from their subsequences. In van Lambalgen (1987), an algorithmic...

Computer Science) Feature Selection for Value Function Approximation

In a recent TINS article 1 , Purves et al. present a critique of darwinian theories of neural development. According to these authors, neural darwinists share the common belief that early in development the nervous system contains an initial excess of neural elements, from which those elements (neurons or synapses) that are less well-suited to the existence of the organism are subsequently elim...

This work analyzes the problem of selecting an adequate neural network architecture for a given function, comparing existing approaches and introducing a new one based on the use of the complexity of the function under analysis. Numerical simulations using a large set of Boolean functions are carried out and a comparative analysis of the results is done according to the architectures that the d...

We study the nature of cluster selection in Sunyaev-Zel'dovich (SZ) surveys, focusing on single frequency observations and using Monte Carlo simulations incorporating instrumental effects, primary cosmic microwave background (CMB) anisotropies and extragalactic point sources. Clusters are extracted from simulated maps with an optimal, multi–scale matched filter. We introduce a general definitio...

For regression models with functional responses and scalar predictors, it is common for the number of predictors to be large. Despite this, few methods for variable selection exist for function-on-scalar models, and none account for the inherent correlation of residual curves in such models. By expanding the coefficient functions using a B-spline basis, we pose the function-on-scalar model as a...