This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed

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Evolution strategies –A comprehensive introduction

Whereas terminological recommendations require authors to use mean intensity or mean abundance to quantify parasites in a sample of hosts, awkward statistical limitations also force them to use either the median or the geometric mean of these measures when making comparisons across different samples. Here, we propose to reconsider this inconsistent practice by giving priority to biological realism in the interpretation of different statistical descriptors and choosing the statistical tools appropriate to our decisions. Prevalence, mean intensity, and indices of parasite distribution (such as median intensity) are suitable descriptors to quantify parasites in a sample of hosts. These measures have different biological interpretations and need different statistical methods to be compared between samples.

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Quantifying parasites in samples of hosts

A concept of generalized order statistics

The availability of position information is of great importance in many commercial, governmental, and military applications Localization is commonly accomplished through the use of radio communication between mobile devices (agents) and fixed infrastructure (anchors) However, precise determination of agent positions is a challenging task, especially in harsh environments due to radio blockage or limited anchor deployment In these situations, cooperation among agents can significantly improve localization accuracy and reduce localization outage probabilities A general framework of analyzing the fundamental limits of wideband localization has been developed in Part I of the paper Here, we build on this framework and establish the fundamental limits of wideband cooperative location-aware networks Our analysis is based on the waveforms received at the nodes, in conjunction with Fisher information inequality We provide a geometrical interpretation of equivalent Fisher information (EFI) for cooperative networks This approach allows us to succinctly derive fundamental performance limits and their scaling behaviors, and to treat anchors and agents in a unified way from the perspective of localization accuracy Our results yield important insights into how and when cooperation is beneficial

http://publications.lib.chalmers.se/records/fulltext/local_133941.pdf

Fundamental Limits of Wideband Localization— Part II: Cooperative Networks

The Breeder Genetic Algorithm (BGA) was designed according to the theories and methods used in the science of livestock breeding. The prediction of a breeding experiment is based on the response to selection (RS) equation. This equation relates the change in a population's fitness to the standard deviation of its fitness, as well as to the parameters selection intensity and realized heritability. In this paper the exact RS equation is derived for proportionate selection given an infinite population in linkage equilibrium. In linkage equilibrium the genotype frequencies are the product of the univariate marginal frequencies. The equation contains Fisher's fundamental theorem of natural selection as an approximation. The theorem shows that the response is approximately equal to the quotient of a quantity called additive genetic variance, VA, and the average fitness. We compare Mendelian two-parent recombination with gene-pool recombination, which belongs to a special class of genetic algorithms that we call univariate marginal distribution (UMD) algorithms. UMD algorithms keep the genotypes in linkage equilibrium. For UMD algorithms, an exact RS equation is proven that can be used for long-term prediction. Empirical and theoretical evidence is provided that indicates that Mendelian two-parent recombination is also mainly exploiting the additive genetic variance. We compute an exact RS equation for binary tournament selection. It shows that the two classical methods for estimating realized heritability---the regression heritability and the heritability in the narrow sense---may give poor estimates. Furthermore, realized heritability for binary tournament selection can be very different from that of proportionate selection. The paper ends with a short survey about methods that extend standard genetic algorithms and UMD algorithms by detecting interacting variables in nonlinear fitness functions and using this information to sample new points.

The equation for response to selection and its use for prediction

A First Course in Order Statistics.

In this article, we present a concise review of developments on discrete multivariate distributions. We first present some basic definitions and notations. Then, we present several important discrete multivariate distributions and list their significant properties and characteristics. \n \n \nKeywords: \n \ngenerating function; \nmoments; \nstirling numbers; \nregression; \ninflated distributions; \ntruncated forms; \ncompound distributions; \nmultinomial; \nnegative multinomial; \nmultivariate poisson; \nmultivariate hypergeometric; \nmultivariate Polya–Eggenberger; \nmultivariate discrete exponential; \nmultivariate power series; \nmultivariate hermite; \nmultivariate occupancy; \nmultivariate weighted; \ndirichlet; \nmultivariate run-related distributions

H. Papageorgiou

Papers

A First Course in Order Statistics.

Discrete Multivariate Distributions