DOCSLIB.ORG

A Design for DNA Computation of the Onemax Problem

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Design for DNA Computation of the Onemax Problem A Design for DNA Computation of the OneMax Problem David Wood Junghuei Chen Eugene Antip ov Bertrand Lemieux Walter Cedeno ~ Computer Sci. Chem. & Bio chem. Chemical Eng. Plant & Soil Sci. Hewlett-Packard Univ. of Del. Univ. of Del. Univ. of Del. Univ. of Del. Centerville Rd Newark DE Newark DE Newark DE Newark DE Wilmington DE Abstract details of the lab oratory op erations, and some prelimi- nary lab oratory results can b e found elsewhere [5, 38]. Elements of evolutionary computation and 1.1 Conventional Evolution Computation molecular biology are combined to design a DNA evolutionary computation. The tradi- Evolution Computation usually maintains a p opula- tional test problem for evolutionary compu- tion of candidate solutions, often represented as strings tation, OneMax problem is addressed. The of binary bits. In each generation, some less promising key feature is the physical separation of DNA candidates are likely to b e eliminated. Their replace- strands consistent with OneMax \ tness." ments are generated from relatively promising candi- dates by cloning and/or mutation and/or recombina- tion. Evolutionary Computation comes in many, many 1 Intro duction di erentvarieties [3, 19, 20], but most are variations on this outline: Evolution is a concept of obtaining adaptation through the interplay of selection and diversity. Analogies from Begin with an initial p opulation, often chosen ran- evolution have b een used in b oth conventional comput- domly. ing and molecular biology. In computing, the general term is \evolutionary computation." The paradigm in Rep eat the following steps until convergence. molecular biology is known as \in vitro evolution." 1. Evaluate tness of candidates. In this pap er we identify elements of evolutionary com- 2. Select candidates to breed, favoring the more putation and molecular biology that we combine to t over the less t. address the traditional OneMax problem. Our design 3. Delete some of the less t. can b e mo di ed to address some other problems as 4. Breed replacements, inducing variation. well [5], as is brie y discussed in Section 5. Wecho ose evolutionary computations that manipulate bitstrings using op erations of p ointwise mutation and crossover. 2 DNA Suits Evolutionary These op erations can b e p erformed by mo di cations Computation of techniques from molecular biology. Section 4.1 forms the heart of this pap er. The cru- Several means of DNA computation have b een ad- cial lab oratory op eration is physically separating DNA dressed. The rst was, of course, by Adleman [1, 2]. strands by their \ tness" for solving the OneMax prob- Recentoverviews can b e found in [17] and [22]. See lem. In this section, we identify two-dimensional de- also the DNA computing bibliography of Dassen and naturing gradient gel electrophoresis as an appropriate Pierluigi [9]. lab oratory technique for this crucial separation. From the b eginning of DNA based computing to the We are careful to make the following p oint. In this pa- present there have b een calls [11,24,29, 6] to consider per we only aspire to provide the design for some evo- carrying out evolutionary computations using genetic lutionary computations using p opulation sizes larger materials in the lab oratory. So far, there have b een than is practical with conventional computers. More three such exp eriment designs prop osed, including two one must physically separate DNA strands according in a recent DIMACS Workshop [4,5]. The very rst to their \ tness" for solving the problem at hand. design was presented ab out twoyears ago [10], but has not yet b een carried out in the lab oratory. 2.2 DNA Evolutionary Computation With our design, computing time using DNA is pro- Compared to Sup ercomputers p ortional to the numb er of generations. This mo- tivates incorp orating b oth p ointwise mutation and The following oversimpli ed estimates indicate DNA crossover, attempting to minimize the numb er of gen- computing techniques could in the future compare erations required. favorably to sup ercomputers in some cases. Favor- For that matter, one mightwant to consider any able cases include executing evolutionary computa- additional evolutionary analogies that might reduce tions having very simple tness evaluations and ex- the numb er of required generations. These might in- tremely large p opulations of candidate solutions. clude transp osition, inversions, introns, etc. These Consider a p opulation represented by a total of p bits. have b een included in researchinevolutionary com- Executing a evolutionary computation by any means putation using conventional computers. Computing will require at least g Op tness evaluations, where paradigms inspired byevolution evolutionary compu- g is the numb er of generations used. Now, assume tations seem particularly suited to implementation us- the tness evaluation of a candidate solution pro cesses ing DNA. This is b ecause evolutionary computations all the bits of the candidate solution. A state-of-the- often use bitstrings, crossover, and p ointwise muta- 12 art tera op sup ercomputer p erforms ab out 10 op er- tion, all of which are done with DNA in nature. ations p er second. Thus, wehave a rough estimate for sup ercomputer time complexity, 2.1 DNA Advantages for Evolutionary Computation 12 17 T g p 10 seconds g p 10 days: 1 DNA computing techniques are desirable for evolution- To compare this to DNA computing, let us assume the ary computation for several reasons, some of which are tness evaluation of an entire p opulation can b e done listed b elow. in the lab oratory in one day [38]. Thus, the time com- plexity of a DNA approachtoevolutionary computa- These techniques might pro cess, in parallel, p op- tion is approximately T g days. Essentially no new ulations which are billions of times larger than is lab oratory techniques or equipmentwould b e needed usual for conventional computers. One exp ecta- to use gram quantities of DNA. This corresp onds to tion for larger p opulations is: they may generate 21 p opulations represented by ab out p =10 bits. For high- tness individuals in fewer generations. p opulations of this size, we see from Eq 1 that the time complexity of DNA implementation of evolution- Massive information storage is available using ary computation T g days compares favorably to DNA. Grams of DNA could b e used. A gram of sup ercomputer time complexityof 21 DNA contains ab out 10 bases. This information 21 content is approximately 2 10 bits, greatly ex- 4 T g 10 days: 2 ceeding the 200 p etabyte storage of all the digital magnetic tap e pro duced in 1995 [37]. In addition, a further factor of 1; 000 may b e consid- ered within reachby using kilograms of DNA. Biolab oratory op erations on DNA inherently in- volve errors. DNA lab oratory errors may b e re- Some caution is needed in interpreting this compar- garded as noise. Noise is more tolerable in execut- ison. The comparison is based on unprecedentedly ing evolutionary computation than in executing large p opulations. Still miniscule compared to the deterministic algorithms. Evolutionary computa- size of the search space, of course! As far as we know, tion has b een found to b e robust in the presence it is unclear exactly how b ene cial large p opulation of noise in some studies [16,15,26,23]. sizes mightbe. The classical \schema theorem" of Holland [20]says a p opulation of P distinct candidates Mo di cations to the current molecular biology 3 prob es O P p otential solutions. However the appli- technology suce to implement crossover and cability of this result, like many others in evolutionary p ointwise mutation [5, 38]. computation, is actively debated [12, 35]. This may b e an appropriate p oint to rep eat our pri- However, selecting DNA strands for \breeding" in evo- mary goal. In this pap er we only aspire to provide the lutionary computation is challenging. This is b ecause design for some evolutionary computations using p op- ulation sizes larger than is practical with conventional computers. - 3 The OneMax Problem This is a traditional test problem for evolutionary com- putation. It involves binary bitstrings of xed length. An initial p opulation usually randomly generated is given. The ob jectiveistoevolve some bitstrings to Electrophoresis match a presp eci ed bitstring generally taken to b e all 1s. It is easy to miss the p oint here: one is not trying to solve a signi cant problem | indeed, the solution is implicit in the problem sp eci cation. OneMax is a + Low High able to arti cial ly classical test to con rm that one is Denaturant Concentration evolve to a solution starting from a given initial popu- lation. Figure 1: DGGE using p erfect matched double stranded DNA. The strands movedownward from a The OneMax problem has b een extensively studied reservoir across the top of the gure. The sp eed of [13, 15, 14], but with assumptions ab out selection that vertical strand migration is retarded as strands come do not seem to hold in our situation. apart denature as shown schematically on the gure. From [5], with p ermission. 4 The OneMax Problem via DNA 4.1 The Heart of the Matter: Physical Separation of DNA by OneMax Fitness by their initial placement from left to right; that is, The crucial part of the DNA implementation of evolu- byhow strongly they are denatured pulled apart. tionary computation is to identify a lab oratory pro cess On the left, where no denaturant is encountered, the that will physically separate DNA strands according strands move relatively quickly downward.
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]
  • 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]
  • 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]
  • 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]
  • Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems
    Cleveland State University EngagedScholarship@CSU Electrical Engineering & Computer Science Electrical Engineering & Computer Science Faculty Publications Department 7-2010 Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems Haiping Ma Shaoxing University, Shaoxing, China, [email protected] Daniel J. Simon Cleveland State University, [email protected] Follow this and additional works at: https://engagedscholarship.csuohio.edu/enece_facpub Part of the Electrical and Computer Engineering Commons How does access to this work benefit ou?y Let us know! Publisher's Statement ACM New York, NY, USA ©2010 Original Citation H. Ma and D. Simon. (2010). Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems. Genetic and Evolutionary Computation Conference, 417-418. Repository Citation Ma, Haiping and Simon, Daniel J., "Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems" (2010). Electrical Engineering & Computer Science Faculty Publications. 184. https://engagedscholarship.csuohio.edu/enece_facpub/184 This Conference Proceeding is brought to you for free and open access by the Electrical Engineering & Computer Science Department at EngagedScholarship@CSU. It has been accepted for inclusion in Electrical Engineering & Computer Science Faculty Publications by an authorized administrator of EngagedScholarship@CSU. For more information, please contact [email protected]. Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems Haiping Ma Dan Simon Shaoxing University Cleveland State University Shaoxing, Zhejiang, 312000, China Cleveland, Ohio [email protected] [email protected] ABSTRACT and which is motivated by blended crossover in GAs [6]. Blended Biogeography-based optimization (BBO) is a new evolutionary migration is defined as algorithm based on the science of biogeography.
    [Show full text]
  • A Short Introduction to Evolutionary Computation (In 50 Minutes)
    A Short Introduction to Evolutionary Computation (in 50 minutes) Fernando Lobo University of Algarve Outline • Why EC in 50 minutes? • The basic idea of a Genetic Algorithm • Application example Why EC in 50 minutes? • If I only had 50 minutes to talk to someone about Evolutionary Computation, this is what I would show them. • Two purposes: – to show you the basic idea quickly, without getting involved in too much details and theory. – to get you very motivated to study this topic. Many variations on EC • There’s a variety of classes of Evolutionary Algorithms – Genetic Algorithms (GAs) – Evolution Strategies (ES) – Evolutionary Programming (EP) – Genetic Programming (GP) – … • In this lecture, we will only look at the so-called Simple Genetic Algorithm. What is a Genetic Algorithm (GA)? • It is a search procedure based on the mechanics of natural selection and genetics. • Often used to solve difficult optimization problems. Evolution • Evolution can be seen as an optimization process where living creatures constantly adapt to their environment. – the stronger survive and propagate their traits to future generations – the weaker die and their traits tend to disappear. • Darwin’s survival of the fittest. How do GAs differ from other techniques? • Work with a population of solutions, rather than a single solution. • Use probabilistic rather than deterministic mechanisms. Two essential components – Selection – Variation • recombination (or crossover) • mutation When should we use a GA? • When other simpler methods are not good enough. • When we don’t have much knowledge about the problem that we are trying to solve. • … and when the search space is very large (making complete enumeration unfeasible).
    [Show full text]
  • A Model of Protocell Based on the Introduction of a Semi-Permeable Membrane in a Stochastic Model of Catalytic Reaction Networks
    A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks Roberto Serra Marco Villani Department of Physics, Informatics and Mathematics Modena and Reggio Emilia University ECLT European Centre for Living Technology S. Marco 2940, University of Ca’ Foscari, Venice (IT) [email protected] [email protected] Alessandro Filisetti ECLT European Centre for Living Technology S. Marco 2940, University of Ca’ Foscari, Venice (IT) [email protected] Alex Graudenzi Chiara Damiani Department of Informatics Systems and Communication, University of Milan Bicocca [email protected] [email protected] Abstract The theoretical characterization of the self-organizing molecular structures emerging from ensembles of distinct interacting chemicals turns to be very important in revealing those dynamics that led to the transition from the non-living to the living matter as well as in the design of artificial protocells [12, 13, 14]. In this work we aim at studying the role of a semi-permeable membrane, i.e. a very simple protocell description, in the dynamics of a stochastic model describing randomly generated catalytic reaction sets (CRSs) of molecules. Catalytic reaction sets are networks composed of different molecules interacting together leading to the formation of several reaction pathways. In particular, molecules are representation of molecular species formed by a concatenation of letters (bricks) from a finite alphabet. Reaction pathways are formed by two kinds of reaction only, namely condensation, where two molecular species are glued together in order to create a longer species and cleavage where a molecular species is divided in two shorter species, Figure 1, Filisetti et al.
    [Show full text]