Modeling, Simulation and Application of Bacterial Transduction in Genetic Algorithms

At present, all methods in Evolutionary Computation are bioinspired in the fundamental principles of neo-Darwinism as well as on a vertical gene transfer. Thus, on a mechanism in which an organism receives genetic material from its ancestor. Horizontal, lateral or cross-population gene transfer is any process in which an organism transfers a genetic segment to another one that is not its offspring. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganism (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring a possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). The efficiency and performance of this algorithm was evaluated using a benchmark function and the 0/1 knapsack problem. The utility was illustrated designing an AM radio receiver, optimizing the main features of the electronic components of the AM radio circuit as well as those of the radio enclosure. Our results shown how PETRI approaches to higher fitness values as transduction probability comes near to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ‘bacterial colonies’

Evolutionary Daisyworld models: A new approach to studying complex adaptive systems

This paper presents a model of a population of error-prone self-replicative species (replicators) that interact with its environment. The population evolves by natural selection in an environment whose change is caused by the evolutionary process itself. For simplicity, the environment is described by a single scalar factor, i.e. its temperature. The formal formulation of the model extends two basic models of Ecology and Evolutionary Biology, namely, Daisyworld and Quasispecies models. It is also assumed that the environment can also change due to external perturbations that are summed up as an external noise. Unlike previous models, the population size self-regulates, so no ad hoc population constraints are involved. When species replication is error-free, i.e. without mutation, the system dynamics can be described by an (n + 1)-dimensional system of differential equations, one for each of the species initially present in the system, and another for the evolution of the environment temperature. Analytical results can be obtained straightforwardly in low-dimensional cases. In these examples, we show the stabilizing effect of thermal white noise on the system behavior. The error-prone self-replication, i.e. with mutation, is studied computationally. We assume that species can mutate two independent parameters: its optimal growth temperature and its influence on the environment temperature. For different mutation rates the system exhibits a large variety of behaviors. In particular, we show that a quasispecies distribution with an internal sub-distribution appears, facilitating species adaptation to new environments. Finally, this ecologically inspired evolutionary model is applied to study the origin and evolution of public opinion

APPENDIX. A SURVEY OF NONPARAMETRIC TESTS FOR THE STATISTICAL ANALYSIS OFEVOLUTIONARY COMPUTATIONAL EXPERIMENTS

<p>Supplementary material of the paper published in International Journal “Information Theories and Applications”, Vol. 17, Number 1, 2010: 49-61</p> <p>One of the main problems in the statistical analysis of Evolutionary Computation (EC) experiments is the ‘statistical personality’ of data. A main feature of EC algorithms is the sampling of solutions from one generation to the next. Sampling is based on Holland’s schema theory, having a greater probability to be chosen those solutions with best-fitness (or evaluation) values. In consequence, simulation experiments result in biased samples with non-normal, highly skewed, and asymmetric distributions. Furthermore, the main problem arises with the noncompliance of one of the main premises of the central limit theorem, invalidating the statistical analysis based on the average fitness f of the solutions. In this paper, we address a tutorial or ‘How-to’ explaining the basics of the statistical analysis of data in EC. The use of nonparametric tests for comparing two or more medians combined with Exploratory Data Analysis is a good option, bearing in mind that we are only considering two experimental situations that are common in EC practitioners: (i) the performance evaluation of an algorithm and (ii) the multiple experiments comparison. The different approaches are illustrated with different examples (see http://bioinformatica.net/tests/survey.html) selected from Evolutionary Computation and the related field of Artificial Life.</p> <p> </p

APPENDIX. MODELING, SIMULATION AND APPLICATION OF BACTERIAL TRANSDUCTION IN GENETIC ALGORITHMS

<p>Supplementary material of the paper published in:</p> <p>International Journal "Information Technologies & Knowledge" Vol.7, Number 1, 2013: 11-22.</p> <p>At present, all methods in Evolutionary Computation are bioinspired by the fundamental principles of neo-Darwinism, as well as by a vertical gene transfer. Virus transduction is one of the key mechanisms of horizontal gene propagation in microorganisms (e.g. bacteria). In the present paper, we model and simulate a transduction operator, exploring the possible role and usefulness of transduction in a genetic algorithm. The genetic algorithm including transduction has been named PETRI (abbreviation of Promoting Evolution Through Reiterated Infection). Our results showed how PETRI approaches higher fitness values as transduction probability comes close to 100%. The conclusion is that transduction improves the performance of a genetic algorithm, assuming a population divided among several sub-populations or ‘bacterial colonies’.</p