Evolving and coevolving computer go players using neuroevolution.

The Go game is ancient very complex game with simple rules which still is a challenge for the AI.This work cover some neuroevolution techniques used in reinforcement learning applied to the GO game as SANE (Symbiotic Adaptive Neuro-Evolution) and presents a variation to this method with the intention of evolving better strategies in the game. The computer Go player based in SANE is evolved againts a knowed player which creates some problem as determinism for which is proposed the co-evolution. Finally, it is introduced an algorithm to co-evolve two populations of neurons to evolve better computer Go players

Applications Of Operations Research/Statistics In Infection Outbreak Management

Operations Research (OR) can be identified as the discipline that uses statistics, mathematics, computer-modelling and similar science methodology for decision making (Luss, Rosenwein, 1997). OR, powered with statistics and models, is a high potential engine for use in many areas that require evidence-based or model-based decision making. One of the most promising areas is specifically the infection outbreak management. Surprisingly, very little OR/statistics research has been aimed at infection outbreak management; usually, other general epidemiology issues were tackled in models. However, OR/statistics models for use in the infection outbreak management exist and can be effectively used in public policy and outbreak management practice. Probably, key reasons for that little involvement of OR/statistics in the infection outbreaks management is low awareness among the specialist community of OR/statistics use and benefits for their decision making. Up to the moment, there is lack of contemporary review of OR/statistics-applied models used for the infection outbreak management decision making. The present paper aimed at filling that gap and providing two benefits to involved health care managers and academics: first, developing awareness on the use and benefits of OR/statistics models for the infection outbreak management decision making, and second, for plotting the current state of affairs to highlight research opportunities for developing the field by academics and epidemic control professionals

A systematic classification of Plasmodium falciparum P-loop NTPases: structural and functional correlation

Background: The P-loop NTPases constitute one of the largest groups of globular protein domains that play highly diverse functional roles in most of the organisms. Even with the availability of nearly 300 different Hidden Markov Models representing the P-loop NTPase superfamily, not many P-loop NTPases are known in Plasmodium falciparum. A number of characteristic attributes of the genome have resulted into the lack of knowledge about this functionally diverse, but important class of proteins. Method: In the study, protein sequences with characteristic motifs of NTPase domain (Walker A and Walker B) are computationally extracted from the P. falciparum database. A detailed secondary structure analysis, functional classification, phylogenetic and orthology studies of the NTPase domain of repertoire of 97 P. falciparum P-loop NTPases is carried out. Results: Based upon distinct sequence features and secondary structure profile of the P-loop domain of obtained sequences, a cladistic classification is also conceded: nucleotide kinases and GTPases, ABC and SMC family, SF½ helicases, AAA+ and AAA protein families. Attempts are made to identify any ortholog(s) for each of these proteins in other Plasmodium sp. as well as its vertebrate host, Homo sapiens. A number of P. falciparum P-loop NTPases that have no homologue in the host, as well as those annotated as hypothetical proteins and lack any characteristic functional domain are identified. Conclusion: The study suggests a strong correlation between sequence and secondary structure profile of P-loop domains and functional roles of these proteins and thus provides an opportunity to speculate the role of many hypothetical proteins. The study provides a methodical framework for the characterization of biologically diverse NTPases in the P. falciparum genome. The efforts made in the analysis are first of its kind; and the results augment to explore the functional role of many of these proteins from the parasite that could provide leads to identify novel drug targets against malaria

Design and Characterisation of a Novel Artificial Life System Incorporating Hierarchical Selection

In this thesis, a minimal artificial chemistry system is presented, which is inspired by the RNA World hypothesis and is loosely based on Holland's Learning Classier Systems. The Molecular Classier System (MCS) takes a bottom-up, individual-based approach to building artificial bio-chemical networks. The MCS has been developed to demonstrate the effects of hierarchical selection. Hierarchical selection appears to have been critical for the evolution of complexity in life as we know it yet, to date, no computational artificial life system has investigated the viability of using hierarchical selection as a mechanism for achieving qualitatively similar results. Hierarchy in MCS is enforced by constraining artificial molecules, which are modeled as individuals, to exist within externally provided containers - protocells. This research is focused on the period of time surrounding the conjectured first Major Transition - from individual replicating molecules to populations of molecules existing within cells. Protocells can be thought of as simplified versions of contemporary biological cells. Molecular replication within these protocells causes them to grow until they undergo a process of binary fission. Darwinian selection is continuously and independently applied at both the molecular level and the protocell level. Experimental results are presented which display the phenomenon of selectional stalemate where the selectional pressures are applied in opposite directions such that they meet in the middle. The work culminates with the presentation of a stable artificial protocell system which is capable of demonstrating ongoing evolution at the protocell level via hierarchical selection of molecular species. Supplementary results are presented in the Appendix material as a set of experiments where selectional pressure is applied at the protocell level in a manner that indirectly favours particular artificial bio-chemical networks at the molecular level. It is shown that a molecular trait which serves no useful purpose to the molecules when they are not contained within protocells is exploited for the benefit of the collective once the molecules are constrained to live together. It is further shown that through the mechanism of hierarchical selection, the second-order effects of this molecular trait can be used by evolution to distinguish between protocells which contain desirable networks, and those that do not. A treatment of the computational potential of such a mechanism is presented with special attention given to the idea that such computation may indeed form the basis for the later evolution of the complicated Cell Signaling Pathways that are exhibited by modern cells

Current perspective of symbiotic organisms search technique in cloud computing environment: a review

Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed

Rules of engagement : competitive coevolutionary dynamics in computational systems

Given that evolutionary biologists have considered coevolutionary interactions since the dawn of Darwinism, it is perhaps surprising that coevolution was largely overlooked during the formative years of evolutionary computing. It was not until the early 1990s that Hillis' seminal work thrust coevolution into the spotlight. Upon attempting to evolve fixed-length sorting networks, a problem with a long and competitive history, Hillis found that his standard evolutionary algorithm was producing sub-standard networks. In response, he decided to reciprocally evolve a population of testlists against the sorting network population; thus producing a coevolutionary system. The result was impressive; coevolution not only outperformed evolution, but the best network it discovered was only one comparison longer than the best-known solution. For the first time, a coevolutionary algorithm had been successfully applied to problem-solving. Pre-Hillis, the shortcomings of standard evolutionary algorithms had been understood for some time: whilst defining an adequate fitness function can be as challenging as the problem one is hoping to solve, once achieved, the accumulation of fitness-improving mutations can push a population towards local optima that are difficult to escape. Coevolution offers a solution. By allowing the fitness of each evolving individual to vary (through competition) with other reciprocally evolving individuals, coevolution removes the requirement of a fitness yardstick. In conjunction, the reciprocal adaptations of each individual begin to erode local optima as soon as they appear. However, coevolution is no panacea. As a problem-solving tool, coevolutionary algorithms suffer from some debilitating dynamics, each a result of the relative fitness assessment of individuals. In a single-, or multi-, population competitive system, coevolution may stabilize at a suboptimal equilibrium, or mediocre stable state; analogous to the traditional problem of local optima. Populations may become highly specialized in an unanticipated (and undesirable) manner; potentially resulting in brittle solutions that are fragile to perturbation. The system may cycle; producing dynamics similar to the children's game rock-paper-scissors. Disengagement may occur, whereby one population out-performs another to the extent that individuals cannot be discriminated on the basis of fitness alone; thus removing selection pressure and allowing populations to drift. Finally, coevolution's relative fitness assessment renders traditional visualization techniques (such as the graph of fitness over time) obsolete; thus exacerbating each of the above problems. This thesis attempts to better understand and address the problems of coevolution through the design and analysis of simple coevolutionary models. 'Reduced virulence' - a novel technique specifically designed to tackle disengagement - is developed. Empirical results demonstrate the ability of reduced virulence to combat disengagement both in simple and complex domains, whilst outperforming the only known competitors. Combining reduced virulence with diversity maintenance techniques is also shown to counteract mediocre stability and over-specialization. A critique of the CIAO plot - a visualization technique developed to detect coevolutionary cycling - highlights previously undocumented ambiguities; experimental evidence demonstrates the need for complementary visualizations. Extending the scope of visualization, a first exploration into coevolutionary steering is performed; a technique allowing the user to interact with a coevolutionary system during run-time. Using a simple model incorporating reduced virulence, the coevolutionary steering demonstration highlights the future potential of such tools for both research and education. The role of neutrality in coevolution is discussed in detail. Whilst much emphasis is placed upon neutral networks in the evolutionary computation literature, the nature of coevolutionary neutrality is generally overlooked. Preliminary ideas for modelling coevolutionary neutrality are presented. Finally, whilst this thesis is primarily aimed at a computing audience, strong reference to evolutionary biology is made throughout. Exemplifying potential crossover, the CIAO plot, a tool previously unused in biology, is applied to a simulation of E. Coli, with results con rming empirical observations of real bacteria.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The role of visual adaptation in cichlid fish speciation

D. Shane Wright (1) , Ole Seehausen (2), Ton G.G. Groothuis (1), Martine E. Maan (1) (1) University of Groningen; GELIFES; EGDB(2) Department of Fish Ecology & Evolution, EAWAG Centre for Ecology, Evolution and Biogeochemistry, Kastanienbaum AND Institute of Ecology and Evolution, Aquatic Ecology, University of Bern.In less than 15,000 years, Lake Victoria cichlid fishes have radiated into as many as 500 different species. Ecological and sexual sel ection are thought to contribute to this ongoing speciation process, but genetic differentiation remains low. However, recent work in visual pigment genes, opsins, has shown more diversity. Unlike neighboring Lakes Malawi and Tanganyika, Lake Victoria is highly turbid, resulting in a long wavelength shift in the light spectrum with increasing depth, providing an environmental gradient for exploring divergent coevolution in sensory systems and colour signals via sensory drive. Pundamilia pundamila and Pundamilia nyererei are two sympatric species found at rocky islands across southern portions of Lake Victoria, differing in male colouration and the depth they reside. Previous work has shown species differentiation in colour discrimination, corresponding to divergent female preferences for conspecific male colouration. A mechanistic link between colour vision and preference would provide a rapid route to reproductive isolation between divergently adapting populations. This link is tested by experimental manip ulation of colour vision - raising both species and their hybrids under light conditions mimicking shallow and deep habitats. We quantify the expression of retinal opsins and test behaviours important for speciation: mate choice, habitat preference, and fo raging performance