October 13rd

14:30 , Amphi Digiteo Shannon (660) (see location):

Flora Jay

Title : Inferring past and present demography from genetic data.

Abstract :

Population genetics is a field that addresses questions related to the genetic variation observed among individuals, and how this variation is shaped by different evolutionary processes such as mutation, recombination, drift, and selection. Understanding the role of demography and adaptation is a major interest of the field, and is under never-ending scrutiny given the constant evolution of available genetic data (both in term of quality and quantity). I will start by giving some insights on how genetic data can help to understand the past and present demographic history of a population. More specifically, I will present a method that aims at inferring the size of a population through time, using the whole-genome data of a few number of individuals. This method relies on Approximate Bayesian Computation (ABC), which is a likelihood-free approach based on simulations. Whole-genome data is extremely rich in information about past demography, and I will show that ABC is a convenient framework to make use of this vast information. I will finish with some future directions of this project that I would like to develop here at the LRI.

16:00 , Amphi or R2014 Digiteo Shannon (660):

Marcus Gallagher

Title : Engineering Features for the Analysis and Comparison Black-box Optimization Problems and Algorithms

Abstract :

Given two black-box optimization algorithms (e.g. A and B ), it is of interest to quantify what kinds of problems A outperforms B on and vice-versa. However, unless the mechanisms of A and B are very well understood and/or strong assumptions made about the structure of the problems, this is a very difficult task. One possible approach is to measure features of optimization problems which capture and measure salient properties of the problems, such that the features can be used to distinguish or categorize problem instances. Subsequently, algorithm performance might be predictable based on such problem features. In this talk I will discuss a recently proposed feature called "length scale". Length scale is based on data available in the black-box setting and makes few, if any, apriori assumptions on the nature of the problem or algorithms to be analysed. Experimental results will be presented showing the potential utility of the analysis across continuous and discrete problems.

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