Evolving dynamic multiple-objective optimization problems with objective replacement

This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation, we suggest the inheritance strategy. When objective replacement occurs, this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement, and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy, where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span

IST Austria Thesis

This thesis concerns itself with the interactions of evolutionary and ecological forces and the consequences on genetic diversity and the ultimate survival of populations. It is important to understand what signals processes leave on the genome and what we can infer from such data, which is usually abundant but noisy. Furthermore, understanding how and when populations adapt or go extinct is important for practical purposes, such as the genetic management of populations, as well as for theoretical questions, since local adaptation can be the first step toward speciation. In Chapter 2, we introduce the method of maximum entropy to approximate the demographic changes of a population in a simple setting, namely the logistic growth model with immigration. We show that this method is not only a powerful tool in physics but can be gainfully applied in an ecological framework. We investigate how well it approximates the real behavior of the system, and find that is does so, even in unexpected situations. Finally, we illustrate how it can model changing environments. In Chapter 3, we analyze the co-evolution of allele frequencies and population sizes in an infinite island model. We give conditions under which polygenic adaptation to a rare habitat is possible. The model we use is based on the diffusion approximation, considers eco-evolutionary feedback mechanisms (hard selection), and treats both drift and environmental fluctuations explicitly. We also look at limiting scenarios, for which we derive analytical expressions. In Chapter 4, we present a coalescent based simulation tool to obtain patterns of diversity in a spatially explicit subdivided population, in which the demographic history of each subpopulation can be specified. We compare the results to existing predictions, and explore the relative importance of time and space under a variety of spatial arrangements and demographic histories, such as expansion and extinction. In the last chapter, we give a brief outlook to further research

from the immune system to neural networks

Storing memory of molecular encounters is vital for an effective response to recurring external stimuli. Interestingly, memory strategies vary among different biological processes. These strategies range from networks that process input signals and retrieve an associative memory to specialized receptors that bind only to related stimuli. The adaptive immune system uses such a specialized strategy and can provide specific responses against many pathogens. During its response, the immune system retains some cells as memory to act quicker when reinfections with the same or evolved pathogens occur. However, differentiation of memory cells remains one of the least understood cell fate decisions in immunology. The ability of immune memory to recognize evolved pathogens makes it an ideal starting point to study learning and memory strategies for evolving environments—a topic with applications far beyond immunology. In this thesis, I present three projects that study different aspects of memory strategies for evolving stimuli. Indeed, we find that specialized memory strategies can follow the evolution of stimuli and reliably recover memory of previous encounters. In contrast, fully connected networks, such as Hopfield networks, fail to reliably recover the memory of evolving stimuli. Thus, pathogen evolution might be the reason that the immune system produces specialized memories. We further find that specialized memory receptors should trade off their maximal binding for cross-reactivity to bind to evolved targets. To produce such receptors, the differentiation into memory cells in the immune system should be highly regulated. Finally, we study update strategies of memory repertoires using an energy-based model. We find that repertoires should have a moderate risk tolerance to fluctuations in performance to adapt to the evolution of targets. Nevertheless, these systems can be very efficient in distinguishing between evolved versions of stored targets and novel random stimuli.2022-01-2

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm

Statistical mechanics of gene competition

Statistical mechanics has been applied to a wide range of systems in physics, biology, medicine and even anthropology. This theory has been recently used to model the complex biochemical processes of gene expression and regulation. In particular, genetic networks offer a large number of interesting phenomena, such as multistability and oscillatory behaviour, that can be modelled with statistical mechanics tools. In the first part of this thesis we introduce gene regulation, genetic switches, and the colonization of a spatially structured media. We also introduce statistical mechanics and some of its useful tools, such as the master equation and mean- field theories. We present simple examples that are both pedagogical and also set the basis for the study of more complicated scenarios. In the second part we consider the exclusive genetic switch, a fundamental example of genetic networks. In this system, two proteins compete to regulate each other's dynamics. We characterize the switch by solving the stationary state in different limits of the protein binding and unbinding rates. We perform a study of the bistability of the system by examining its probability distribution, and by applying information theory techniques. We then present several versions of a mean field theory that offers further information about the switch. Finally, we compute the stationary probability distribution with an exact perturbative approach in the unbinding parameter, obtaining a valid result for a wide range of parameters values. The techniques used for this calculation are successfully applied to other switches. The topic studied in the third part of the thesis is the propagation of a trait inside an expanding population. This trait may represent resistance to an antibiotic or being infected with a certain virus. Although our model accounts for different examples in the genetic context, it is also very useful for the general study of a trait propagating in a population. We compute the speed of expansion and the stationary population densities for the invasion of an established and an expanding population, finding non-trivial criteria for speed selection and interesting speed transitions. The obtained formulae for the different wave speeds show excellent agreement with the results provided by simulations. Moreover, we are able to obtain the value of the speeds through a detailed analysis of the populations, and establish the requirements for our equations to present speed transitions. We finally apply our model to the propagation in a position-dependent fitness landscape. In this situation, the growth rate or the maximum concentration depends on the position. The amplitudes and speeds of the waves are again successfully predicted in every case

Evolutionary branching under slow directional evolution

Evolutionary branching is the process by which ecological interactions induce evolutionary diversification. In asexual populations with sufficiently rare mutations, evolutionary branching occurs through trait-substitution sequences caused by the sequential invasion of successful mutants. A necessary and sufficient condition for evolutionary branching of univariate traits is the existence of a convergence stable trait value at which selection is locally disruptive. Real populations, however, undergo simultaneous evolution in multiple traits. Here we extend conditions for evolutionary branching to bivariate trait spaces in which the response to disruptive selection on one trait can be suppressed by directional selection on another trait. To obtain analytical results, we study trait-substitution sequences formed by invasions that possess maximum likelihood. By deriving a sufficient condition for evolutionary branching of bivariate traits along such maximum-likelihood-invasion paths (MLIPs), we demonstrate the existence of a threshold ratio specifying how much disruptive selection in one trait direction is needed to overcome the obstruction of evolutionary branching caused by directional selection in the other trait direction. Generalizing this finding, we show that evolutionary branching of bivariate traits can occur along evolutionary-branching lines on which residual directional selection is sufficiently weak. We then present numerical analyses showing that our generalized condition for evolutionary branching is a good indicator of branching likelihood even when trait-substitution sequences do not follow MLIPs and when mutations are not rare. Finally, we extend the derived conditions for evolutionary branching to multivariate trait spaces