DOCSLIB.ORG

Harnessing Digital Evolution

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Harnessing Digital Evolution C O V E R F E A T U R E Harnessing Digital Evolution Philip McKinley, Betty H.C. Cheng, Charles Ofria, David Knoester, Benjamin Beckmann, and Heather Goldsby Michigan State University In digital evolution, self-replicating computer programs—digital organisms—experience mutations and selective pressures, potentially producing computational systems that, like natural organisms, adapt to their environment and protect themselves from threats. Such organisms can help guide the design of computer software. early 150 years ago, Charles Darwin plasticity and in the longer term through Darwinian evo- explained how evolution and natural selec- lution. Indeed, no existing cybersystem rivals the com- tion transformed the earliest life forms into plexity of Earth’s biosphere, yet life on Earth has evolved the rich panoply of life seen today. Scientists to not only deal with this complexity but to thrive on it. estimate this process has been at work on Many researchers have studied how to use the charac- NEarth for at least 3.5 billion years. teristics of natural systems to design better computing But we remain at the dawn of evolution in another systems. One approach mimics the behaviors of social world: the world of computing. There, evolution helps insects and other species. However, while such biomi- humans solve complex problems in engineering and metic methods have shown promise in controlling fleets provides insight into the evolutionary process in nature. of unmanned robotic systems and in other applications, As computing power continues to increase, research- they can only codify behaviors observed in nature today. ers and developers apply evolutionary algorithms to an Purely biomimetic approaches seek to imitate the results ever-widening variety of problems. As the “Evolution of evolution, but they do not account for the process of in a Computer” sidebar shows, evolutionary computa- natural selection that produced those behaviors. tion methods such as genetic algorithms have already For example, we can design the control software on achieved considerable success, rivaling and surpassing a microrobot so that it mimics certain behaviors found human designers in problem domains as wide-ranging in ants. However, while the robot might possess some as flash memory sticks and aircraft wings. physical characteristics reminiscent of an ant, the dif- We are investigating how to harness the power of ferences vastly outnumber the similarities. On the other evolution to help construct better computer software. hand, if we had the ability to evolve the control software, The increasing interaction between computing technol- taking into account the capabilities of the robot and the ogy and the physical world motivates this work. Sys- characteristics of its environment, new behaviors might tems must adapt to their environment, compensate for emerge that more effectively control the robot.3 failures, optimize performance, and protect themselves from attacks—all with minimal human intervention.1,2 DIGITAL PETRI DISH To design robust and resilient computational systems, Digital evolution gives us this power, and we are inves- we can take inspiration from nature. Living organisms tigating how it can aid us in designing robust compu- have an amazing ability to adapt to changing envi- tational systems. Digital evolution is a form of evolu- ronments, both in the short term through phenotypic tionary computation in which self-replicating computer 54 Computer Published by the IEEE Computer Society 0018-9162/08/$25.00 © 2008 IEEE Evolution in a Computer Evolutionary computation,1 a subfield of computer Core War, which Steen Rasmussen extended in 1990 science, applies the basic principles of genetic evolu- into a system he called Core World. Soon after, Thomas tion to problem solving. EC is based on evolutionary Ray designed Tierra, which used a streamlined and biology and extends into many other fields, includ- fault-tolerant genetic language. ing artificial life. In general, an EC system contains In 1993, Charles Ofria, Chris Adami, and C. Titus one or more populations of individuals that compete Brown began developing the Avida digital-evolution for resources in a computational environment. These platform4 at the California Institute of Technology. In individuals produce offspring according to their fit- Avida, each program lives in its own address space, ness, which often depends on the problem domain. unlike Tierra’s shared address space. This enhance- The most well-known evolutionary computation ment increased the power of digital evolution as method is the genetic algorithm.2 In this iterative an experimental tool. Avida has since been used to search technique the individuals in the population conduct pioneering research in the evolution of bio- are encodings of candidate solutions to an optimiza- complexity, with an emphasis on understanding the tion problem. In each generation, the fitness of every evolutionary design process in nature. In addition to individual is calculated, and a subset of individuals is providing a tool for biologists, digital evolution pro- selected, recombined, or mutated, and moved to the vides an open-ended search technique for problems next generation. in science and engineering. Genetic programming3 provides a related method in which the individuals are actual computer pro- References grams. These approaches and other EC methods have 1. K.A. De Jong, Evolutionary Computation: A Unified been used to solve complex problems, in some cases Approach, MIT Press, 2002. producing patentable designs.3 The annual Genetic 2. J.H. Holland, Adaptation in Natural and Artificial Systems: and Evolutionary Computation Conference gives An Introductory Analysis with Applications to Biology, Con- awards for human-competitive results produced trol, and Artificial Intelligence, Univ. Michigan Press, 1975. by genetic and evolutionary computation (www. 3. J.R. Koza et al., Genetic Programming IV: Routine Human- geneticprogramming.org/hc2007/cfe2007.html). Competitive Machine Intelligence, Genetic Programming, While the broad field of evolutionary computation Springer, 2005. has been studied extensively since the 1960s, the 4. C. Ofria and C.O. Wilke, “Avida: A Software Platform for subfield of digital evolution is much younger. Self-rep- Research in Computational Evolutionary Biology,” J. Arti- licating digital organisms can be traced to the game ficial Life, vol. 10, 2004, pp. 191-229. programs evolve within a user-defined computational these interactions, an organism can gain or lose virtual environment.4 These digital organisms receive limited CPU cycles, affecting how fast it executes instructions. resources whose use must be carefully balanced if they The virtual CPU architecture used in most of our stud- are to survive. As organisms replicate, instruction-level ies is simple, containing three general-purpose registers mutations produce variation within the population. {AX, BX, CX}, two general-purpose stacks {GS, LS}, and Over generations, natural selection can produce instruc- four special-purpose heads. These heads serve as point- tion sequences that can realize complex behaviors, some- ers into the organism’s genome and resemble a tradi- times revealing unexpected and strikingly clever strate- tional program counter or stack pointer. The instruction gies for solving problems. set for this virtual CPU is Turing-complete, and there- Our work uses and extends the Avida digital evolu- fore, theoretically, it can realize any computable func- tion platform. Figure 1 depicts an Avida population and tion. Available instructions perform basic computational the structure of an individual organism. Each digital tasks (addition, multiplication, and bit-shifts), control organism consists of a circular list of instructions—its execution flow, enable communication, and allow for genome—and a virtual CPU, which executes the instruc- replication. Although the instruction set resembles a tra- tions. An Avida environment comprises several cells, ditional assembly language, it is designed so that random each of which can contain at most one organism, or mutations (inserting, deleting, or changing instructions) Avidian. When an Avidian replicates, the system places will always yield a syntactically correct program. the offspring in a randomly selected cell, terminating Avidians receive virtual CPU-cycle rewards for per- any previous inhabitant. Organisms can send messages forming user-defined tasks, generally defined in terms of to each other, produce and consume resources, and sense the organisms’ externally visible behaviors—their pheno- and change their environment’s properties. Through type. For example, a task might require the organism to January 2008 55 C O V E R F E A T U R E p sub p sub k shift−r h push q nand e if−less Registers b nop−B C rotate−r AX Stacks m inc s h−alloc CPU t h−divide BX GS a nop−A y get−head CX LS eo a d d if−less Ayre t r i e gvsee−ttm−shfgleoawd Fzg e t − igidf−elta−bidel c nop−C Heads Dii f − g rs tsweanpd−−smtksg vmE o v − hre−eatdsreieavrceh−msg Instruction F get−id f if−grt Flow−control Cell w mov−head z if−label interface u h−copy Read u h−copy u h−copy Write D send−msg u h−copy D send−msg z if−label u h−copy C rotate−r Figure 1. Elements of the Avida digital evolution platform: (top) structure of an individual organism and (bottom) a population of digital organisms in an 8 t 10 grid of cells. Different colors represent organisms with different genomes. perform a particular mathematical or logical operation to future generations. Avidians that are most success- and output the result, or communicate with neighboring ful—those that replicate faster, or perform user-defined organisms in a manner helpful to distributed problem tasks—are more likely to spread throughout and even- solving. Tasks create selective pressures in the popula- tually dominate the population. Indeed, Avida satisfies tion, favoring genomes where mutations have produced the three conditions necessary for evolution to occur5: sequences of instructions that complete tasks. replication, variation (mutation), and differential fitness In our studies, evolved code segments for completing (competition).
Load more

Recommended publications
  • Completely Derandomized Self-Adaptation in Evolution Strategies
    Completely Derandomized Self-Adaptation in Evolution Strategies Nikolaus Hansen [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Andreas Ostermeier [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Abstract This paper puts forward two useful methods for self-adaptation of the mutation dis- tribution – the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation dis- tributions are developed. Applying arbitrary, normal mutation distributions is equiv- alent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigor- ously, a completely derandomized self-adaptation scheme results, which adapts arbi- trary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation – utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their per- formances are comparable only on perfectly scaled functions. On badly scaled, non- separable functions usually a speed up factor of several orders of magnitude is ob- served. On moderately mis-scaled functions a speed up factor of three to ten can be expected. Keywords Evolution strategy, self-adaptation, strategy parameter control, step size control, de- randomization, derandomized self-adaptation, covariance matrix adaptation, evolu- tion path, cumulation, cumulative path length control.
    [Show full text]
  • A New Biogeography-Based Optimization Approach Based on Shannon-Wiener Diversity Index to Pid Tuning in Multivariable System
    ABCM Symposium Series in Mechatronics - Vol. 5 Section III – Emerging Technologies and AI Applications Copyright © 2012 by ABCM Page 592 A NEW BIOGEOGRAPHY-BASED OPTIMIZATION APPROACH BASED ON SHANNON-WIENER DIVERSITY INDEX TO PID TUNING IN MULTIVARIABLE SYSTEM Marsil de Athayde Costa e Silva, [email protected] Undergraduate course of Mechatronics Engineering Camila da Costa Silveira, [email protected] Leandro dos Santos Coelho, [email protected] Industrial and Systems Engineering Graduate Program, PPGEPS Pontifical Catholic University of Parana, PUCPR Imaculada Conceição, 1155, Zip code 80215-901, Curitiba, Parana, Brazil Abstract. Proportional-integral-derivative (PID) control is the most popular control architecture used in industrial problems. Many techniques have been proposed to tune the gains for the PID controller. Over the last few years, as an alternative to the conventional mathematical approaches, modern metaheuristics, such as evolutionary computation and swarm intelligence paradigms, have been given much attention by many researchers due to their ability to find good solutions in PID tuning. As a modern metaheuristic method, Biogeography-based optimization (BBO) is a generalization of biogeography to evolutionary algorithm inspired on the mathematical model of organism distribution in biological systems. BBO is an evolutionary process that achieves information sharing by biogeography-based migration operators. This paper proposes a modification for the BBO using a diversity index, called Shannon-wiener index (SW-BBO), for tune the gains of the PID controller in a multivariable system. Results show that the proposed SW-BBO approach is efficient to obtain high quality solutions in PID tuning. Keywords: PID control, biogeography-based optimization, Shannon-Wiener index, multivariable systems.
    [Show full text]
  • Genetic Algorithms for Applied Path Planning a Thesis Presented in Partial Ful Llment of the Honors Program Vincent R
    Genetic Algorithms for Applied Path Planning A Thesis Presented in Partial Ful llment of the Honors Program Vincent R. Ragusa Abstract Path planning is the computational task of choosing a path through an environment. As a task humans do hundreds of times a day, it may seem that path planning is an easy task, and perhaps naturally suited for a computer to solve. This is not the case however. There are many ways in which NP-Hard problems like path planning can be made easier for computers to solve, but the most signi cant of these is the use of approximation algorithms. One such approximation algorithm is called a genetic algorithm. Genetic algorithms belong to a an area of computer science called evolutionary computation. The techniques used in evolutionary computation algorithms are modeled after the principles of Darwinian evolution by natural selection. Solutions to the problem are literally bred for their problem solving ability through many generations of selective breeding. The goal of the research presented is to examine the viability of genetic algorithms as a practical solution to the path planning problem. Various modi cations to a well known genetic algorithm (NSGA-II) were implemented and tested experimentally to determine if the modi cation had an e ect on the operational eciency of the algorithm. Two new forms of crossover were implemented with positive results. The notion of mass extinction driving evolution was tested with inconclusive results. A path correction algorithm called make valid was created which has proven to be extremely powerful. Finally several additional objective functions were tested including a path smoothness measure and an obstacle intrusion measure, the latter showing an enormous positive result.
    [Show full text]
  • Chapter 6.Qxp
    Testing Darwin Digital organisms that breed thousands of times faster than common bacteria are beginning to shed light on some of the biggest unanswered questions of evolution BY CARL ZIMMER F YOU WANT TO FIND ALIEN LIFE-FORMS, does this. Metabolism? Maybe not quite yet, but Ihold off on booking that trip to the moons of getting pretty close.” Saturn. You may only need to catch a plane to East Lansing, Michigan. One thing the digital organisms do particularly well is evolve. “Avida is not a simulation of evolu- The aliens of East Lansing are not made of car- tion; it is an instance of it,” Pennock says. “All the bon and water. They have no DNA. Billions of them core parts of the Darwinian process are there. These are quietly colonizing a cluster of 200 computers in things replicate, they mutate, they are competing the basement of the Plant and Soil Sciences building with one another. The very process of natural selec- at Michigan State University. To peer into their tion is happening there. If that’s central to the defi- world, however, you have to walk a few blocks west nition of life, then these things count.” on Wilson Road to the engineering department and visit the Digital Evolution Laboratory. Here you’ll It may seem strange to talk about a chunk of find a crew of computer scientists, biologists, and computer code in the same way you talk about a even a philosopher or two gazing at computer mon- cherry tree or a dolphin. But the more biologists itors, watching the evolution of bizarre new life- think about life, the more compelling the equation forms.
    [Show full text]
  • The Roles of Evolutionary Computation, Fitness Landscape, Constructive Methods and Local Searches in the Development of Adaptive Systems for Infrastructure Planning
    International Symposium for Next Generation Infrastructure October 1-4, 2013, Wollongong, Australia The roles of evolutionary computation, fitness landscape, constructive methods and local searches in the development of adaptive systems for infrastructure planning Mehrdad Amirghasemi a* Reza Zamani a Abstract: Modelling and systems simulation for improving city planning, and traffic equilibrium are involved with the development of adaptive systems that can learn and respond to the environment intelligently. By employing sophisticated techniques which highly support infrastructure planning and design, evolutionary computation can play a key role in the development of such systems. The key to presenting solution strategies for these systems is fitness landscape which makes some problems hard and some problems easy to tackle. Moreover, constructive methods and local searches can assist evolutionary searches to improve their performance. In this paper, in the context of infrastructure, in general, and city planning, and traffic equilibrium, in particular, the integration of these four concepts is briefly discussed. Key words: Evolutionary computation; Local search; Infrastructure; City planning; Traffic equilibrium. I. Introduction Infrastructure is a term used to indicate both organizational and physical structures a society or an enterprise needs to function. In effect, all facilities and services needed for the operation of an economy is gathered under the umbrella term of “Infrastructure”. Each Infrastructure includes connected elements which are structurally related and each element can affect the other elements. It is through this interconnection that these elements collectively provide a basis for structural development of a society or an enterprise. Viewed in an operative level, infrastructure makes the production of goods and services required in the society more straightforward and the distribution of finished products to market easier.
    [Show full text]
  • Editorial on Digital Organism
    Editorial Journal of Computer Science & Volume 13:6, 2020 DOI: 10.37421/jcsb.2020.13.325 Systems Biology ISSN: 0974-7230 Open Access Editorial on Digital Organism Chinthala Mounica* Department of Computer Science, Osmania University, India a growing number of evolutionary biologists. Evolutionary biologist Richard Editorial Note Lenski of Michigan State University has used Avida extensively in his work. Lenski, Adami, and their colleagues have published in journals such as Nature An advanced creature is a self-duplicating PC program that changes and and the Proceedings of the National Academy of Sciences (USA). Digital develops. Advanced creatures are utilized as an apparatus to contemplate the organisms can be traced back to the game Darwin, developed in 1961 at elements of Darwinian development, and to test or check explicit speculations Bell Labs, in which computer programs had to compete with each other by or numerical models of development. The investigation of computerized trying to stop others from executing. A similar implementation that followed creatures is firmly identified with the region of counterfeit life. this was the game Core War. In Core War, it turned out that one of the winning Digital organisms can be traced back to the game Darwin, developed in strategies was to replicate as fast as possible, which deprived the opponent of 1961 at Bell Labs, in which computer programs had to compete with each other all computational resources. Programs in the Core War game were also able to by trying to stop others from executing. A similar implementation that followed mutate themselves and each other by overwriting instructions in the simulated this was the game Core War.
    [Show full text]
  • Sustainable Growth and Synchronization in Protocell Models
    life Article Sustainable Growth and Synchronization in Protocell Models Roberto Serra 1,2,3 and Marco Villani 1,2,* 1 Department of Physics, Informatics and Mathematics, Modena and Reggio Emilia University, Via Campi 213/A, 41125 Modena, Italy 2 European Centre for Living Technology, Ca’ Bottacin, Dorsoduro 3911, Calle Crosera, 30123 Venice, Italy 3 Institute for Advanced Study, University of Amsterdam, Oude Turfmarkt 147, 1012 GC Amsterdam, The Netherlands * Correspondence: [email protected] Received: 19 July 2019; Accepted: 14 August 2019; Published: 21 August 2019 Abstract: The growth of a population of protocells requires that the two key processes of replication of the protogenetic material and reproduction of the whole protocell take place at the same rate. While in many ODE-based models such synchronization spontaneously develops, this does not happen in the important case of quadratic growth terms. Here we show that spontaneous synchronization can be recovered (i) by requiring that the transmembrane diffusion of precursors takes place at a finite rate, or (ii) by introducing a finite lifetime of the molecular complexes. We then consider reaction networks that grow by the addition of newly synthesized chemicals in a binary polymer model, and analyze their behaviors in growing and dividing protocells, thereby confirming the importance of (i) and (ii) for synchronization. We describe some interesting phenomena (like long-term oscillations of duplication times) and show that the presence of food-generated autocatalytic cycles is not sufficient to guarantee synchronization: in the case of cycles with a complex structure, it is often observed that only some subcycles survive and synchronize, while others die out.
    [Show full text]
  • Arxiv:1803.03453V4 [Cs.NE] 21 Nov 2019
    The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Joel Lehman1†, Jeff Clune1, 2†, Dusan Misevic3†, Christoph Adami4, Lee Altenberg5, Julie Beaulieu6, Peter J Bentley7, Samuel Bernard8, Guillaume Beslon9, David M Bryson4, Patryk Chrabaszcz11, Nick Cheney2, Antoine Cully12, Stephane Doncieux13, Fred C Dyer4, Kai Olav Ellefsen14, Robert Feldt15, Stephan Fischer16, Stephanie Forrest17, Antoine Fr´enoy18, Christian Gagn´e6 Leni Le Goff13, Laura M Grabowski19, Babak Hodjat20, Frank Hutter11, Laurent Keller21, Carole Knibbe9, Peter Krcah22, Richard E Lenski4, Hod Lipson23, Robert MacCurdy24, Carlos Maestre13, Risto Miikkulainen26, Sara Mitri21, David E Moriarty27, Jean-Baptiste Mouret28, Anh Nguyen2, Charles Ofria4, Marc Parizeau 6, David Parsons9, Robert T Pennock4, William F Punch4, Thomas S Ray29, Marc Schoenauer30, Eric Schulte17, Karl Sims, Kenneth O Stanley1,31, Fran¸coisTaddei3, Danesh Tarapore32, Simon Thibault6, Westley Weimer33, Richard Watson34, Jason Yosinski1 †Organizing lead authors 1 Uber AI Labs, San Francisco, CA, USA 2 University of Wyoming, Laramie, WY, USA 3 Center for Research and Interdisciplinarity, Paris, France 4 Michigan State University, East Lansing, MI, USA 5 Univeristy of Hawai‘i at Manoa, HI, USA 6 Universit´eLaval, Quebec City, Quebec, Canada 7 University College London, London, UK 8 INRIA, Institut Camille Jordan, CNRS, UMR5208, 69622 Villeurbanne, France 9 Universit´ede Lyon, INRIA, CNRS, LIRIS UMR5205, INSA, UCBL,
    [Show full text]
  • A Note on Evolutionary Algorithms and Its Applications
    Bhargava, S. (2013). A Note on Evolutionary Algorithms and Its Applications. Adults Learning Mathematics: An International Journal, 8(1), 31-45 A Note on Evolutionary Algorithms and Its Applications Shifali Bhargava Dept. of Mathematics, B.S.A. College, Mathura (U.P)- India. <[email protected]> Abstract This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas. Key words: evolutionary algorithms, multi-objective optimization, pareto-optimality, elitist. Introduction The term evolutionary algorithm (EA) stands for a class of stochastic optimization methods that simulate the process of natural evolution. The origins of EAs can be traced back to the late 1950s, and since the 1970’s several evolutionary methodologies have been proposed, mainly genetic algorithms, evolutionary programming, and evolution strategies. All of these approaches operate on a set of candidate solutions. Using strong simplifications, this set is subsequently modified by the two basic principles of evolution: selection and variation. Selection represents the competition for resources among living beings. Some are better than others and more likely to survive and to reproduce their genetic information. In evolutionary algorithms, natural selection is simulated by a stochastic selection process. Each solution is given a chance to reproduce a certain number of times, dependent on their quality. Thereby, quality is assessed by evaluating the individuals and assigning them scalar fitness values. The other principle, variation, imitates natural capability of creating “new” living beings by means of recombination and mutation.
    [Show full text]
  • Analysis of Evolutionary Algorithms
    Why We Do Not Evolve Software? Analysis of Evolutionary Algorithms Roman V. Yampolskiy Computer Engineering and Computer Science University of Louisville [email protected] Abstract In this paper, we review the state-of-the-art results in evolutionary computation and observe that we don’t evolve non-trivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being solved as currently attempted. A detailed analysis of necessary and available computational resources is provided to support our findings. Keywords: Darwinian Algorithm, Genetic Algorithm, Genetic Programming, Optimization. “We point out a curious philosophical implication of the algorithmic perspective: if the origin of life is identified with the transition from trivial to non-trivial information processing – e.g. from something akin to a Turing machine capable of a single (or limited set of) computation(s) to a universal Turing machine capable of constructing any computable object (within a universality class) – then a precise point of transition from non-life to life may actually be undecidable in the logical sense. This would likely have very important philosophical implications, particularly in our interpretation of life as a predictable outcome of physical law.” [1]. 1. Introduction On April 1, 2016 Dr. Yampolskiy posted the following to his social media accounts: “Google just announced major layoffs of programmers. Future software development and updates will be done mostly via recursive self-improvement by evolving deep neural networks”. The joke got a number of “likes” but also, interestingly, a few requests from journalists for interviews on this “developing story”.
    [Show full text]
  • CLAUS O. WILKE Section of Integrative Biology, Center For
    CLAUS O. WILKE Section of Integrative Biology, Center for Computational Biology and Bioinformatics, and Institute for Cellular and Molecular Biology University of Texas at Austin 1 University Station C0930 Austin, TX 78712 [email protected] Education Ph.D. in Theoretical Physics Ruhr-Universitat¨ Bochum June 1999 Title: Evolutionary Dynamics in Time-Dependent Environments. Adviser: Thomas Martinetz Diplom in Theoretical Physics Ruhr-Universitat¨ Bochum Nov. 1996 (The German Diplom is comparable to a M.S.) Employment 2005–present Assistant Professor, University of Texas at Austin. 2004–2005 Research Assistant Professor, Keck Graduate Institute. 2003–2004 Senior Postdoctoral Scholar, California Institute of Technology. 2000–2002 Postdoctoral Scholar, California Institute of Technology. 1999 Postdoctoral Fellow, Medizinische Universitat¨ Lubeck,¨ Germany. 1996–1999 Research Assistant, Ruhr-Universitat¨ Bochum, Germany. Awards 2010 College of Natural Sciences Teaching Excellence Award, UT Austin 2010/2011 ICMB Fellowship, UT Austin. 2007/2008 Reeder Centennial Fellowship in Systematic and Evolutionary Biology, UT Austin. 2003 Los Alamos National Laboratory Director’s funded Postdoc (declined). 1999 Ph.D. in Theoretical Physics with Highest Honors (”Ausgezeichnet”), University of Bochum, Germany. 1996 Diplom in Theoretical Physics with Highest Honors (”Ausgezeichnet”), University of Bochum, Germany. 1996 Ruth and Gerd Massenberg award for excellence in physics, University of Bochum, Germany. 1994/1995 Erasmus scholarship, University of Sussex. 2 Claus O. Wilke, Section of Integrative Biology, The University of Texas at Austin Grants Funded 6. BEACON, An NSF Center for the Study of Evolution in Action. NSF. Erik Goodman (PI). Project period: 08/01/2010–07/31/2015; total award amount: $25 million (to MSU and 4 partner universities including UT).
    [Show full text]
  • Genetic Algorithms and Evolutionary Computation
    126 IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.12, December 2010 Genetic Algorithms and Evolutionary Computation Ms.Nagori Meghna, Faculty of EngineeringGovernment EnggCollege Aurangabad, -431432, India Ms. KaleJyoti Faculty of Engineering MGM’c Engg College Nanded, -411402, India Abstract from the wild type), and dairy cows (with immense udders Generally speaking, genetic algorithms are simulations of far larger than would be required just for nourishing evolution, of what kind ever. In most cases, however, genetic offspring).Critics might charge that creationists can algorithms are nothing else than probabilistic optimization explain these things without recourse to evolution. For methods which are based on the principles of evolution. Using example, creationists often explain the development of simulation and Genetic Algorithms to improve cluster tool resistance to antibiotic agents in bacteria, or the changes performance, Mooring Pattern Optimization using Genetic wrought in domesticated animals by artificial selection, by Algorithms. This paper is designed to cover a few important application aspects of genetic algorithm under a single umbrella. presuming that God decided to create organisms in fixed Key words: groups, called "kinds" or baramin. Though natural Genetic Algorithm microevolution or human-guided artificial selection can bring about different varieties within the originally created "dog-kind," or "cow-kind," or "bacteria-kind" (!), Introduction no amount of time or genetic change can transform one "kind" into another. However, exactly how the creationists determine what a "kind" is, or what mechanism prevents Creationists occasionally charge that evolution is useless living things from evolving beyond its boundaries, is as a scientific theory because it produces no practical invariably never explained.
    [Show full text]