Electronics Spectra - SMS Lucknow

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SMS Institute of Technology, L ucknow Department of Electronics & Co mmunication Genetic algorithms INTRODUCTION GENETIC algorithms are one of the best ways to solve a problem for which little is known. They are a very general algorithm and so will work well in any search space. All you need to know is the solution to be able to do well, and a genetic algorithm will be able to create a high quality solution. Genetic algorithms use the principles of selection and evolution to produce several solutions to a given problem. Genetic Algorithms are a way of solving problems by mimicking the same processes which Mother Nature uses. They use the same combination of selection, recombination and mutation to evolve a solution to a prob - lem. Genetic algorithms tend to thrive in an environment in which there is a very large set of candidates solutions and in which the search space is uneven and has many hills and valleys. Geneti c algorithms will do well in any environment, but they will be greatly outclassed by more situation specific algorithms in the simpler search spaces. Therefore, you must keep in mind that genetic algorithms are not always the best choice. Sometimes they can take quite a while to run and are therefore not always feasible for real time use. They are, however, one of the most powerful methods which quickly create high quality solutions to a problem. Now, before we start, I’m going to provide you with some key terms so that this article makes a sense. ‣ Individual - Any possible solution ‣ Population - Group of all individuals ‣ Search Space - All possible solutions to the problem ‣ Chromosome - Blueprint for an individual ‣ Trait - Possible aspect of an individual ‣ Allele - Possible settings for a trait ‣ Locus - The position of a gene on the chromosome ‣ Genome - Collection of all chr o- mosomes for an individual FOUNDATIONS IN SCIENCE In the mid 1800s, 1859 to be exact, a British naturalist named Charles Darwin published a book that changed the humans view to the world. In this book, The Origin of Species, D arwin proposed that humans, and in fact all creatures, were not put on this planet Sudhanshu Singh EC - III year by God and made unchanging, bu t rather that they evolved from other creatures. At the time, the id ea sounded preposterous, but later we have discovered that he may be correct. Advancement in technolog y have made it possible for us t o read our DNA and that of other creatures, and what it has shown us is that we aren’t as different from other creatures as we think. Over time, creatures change to adapt to their environment to survive and thrive. One of the most striking examples of this is the Galapagos Islands, located in the Pacific Ocean, off the coast of Ecuador, this series of islands is one of the most prominent examples of evolution and adaptation. The island contains many species which are n ot found anywhere else on the pla net, including several species of birds that share many characteristics; too many for it to be a coincidence. It is believed that many birds were blown to the islands by winds and were unable to get back. Over time, the birds spread throughout the island and began survive in the differing environments of the islands. Some birds developed large, strong beaks suited to crack nuts, others long, narrow beaks more suitable for digging bugs out of wood. The birds that had these characteristics when blown to the island survived longer than other birds. This allowed them to reproduce more and therefore have more offspring that also had this unique characteristic. Those without the characteristic gradually died out from starvation. Eventually all of the birds had a type of beak that helped it survive on its island. This is the process of natural selection and evolution. The individuals themselves do not change, but those that survive better, or have a higher fitness, will survive longer and produce more offspring. This continues to happen, with the individuals becoming more suited to their environment every generation. It was this continuous improvement that inspired computer scientists, one of the most prominent being John Holland, to create genetic algorithms. WHY A GENETIC ALGORITHM? Advances in computer technology have made molecular dynamics simulations more and more popular in 26 Electronics Spectra, 2010
SMS Institute of Technology, L ucknow Department of Electronics & Co mmunication studying the behavior of complex systems. Even with modern-day computers, however, there are still two main limitations facing atomistic simulations: system size and simulation time. While recent developments in parallel computer design and algorithms ha ve made considerable progress in enlarging the system size that can b e accessed using atomistic simulat ions, methods for shortening the simulation time still remain relatively unexplored. One example where such methods will be useful is in the determination of the lowest energy conf igurations of a collection of atoms. Because the number of candidate local energy minima grows exponentially with the number of atoms, the computational effort scales exponentially with problem size, making it a member of the NP-hard problem class. For a few atoms, the ground state can sometimes be found by a br ute force search of configuration space. For up to ten or twenty atoms, depending upon the potential, simulated annealing may be employed to g enerate some candidate ground st ate configurations. For more atoms than this, attempts to use simulate d annealing to find the global energy minimum are frustrated by high ene rgy barriers which trap the simulation in one of the numerous metastable configurations. An algorithm is needed which can ‘hop’ from one minimum to anot her and permit an efficient sampli ng of phase space. Our approach is based on the genetic algorithm (GA), an optimization strategy inspired by the Darwinian evolution process. Starting with a population of candidate structures, we relax these candidat es to the nearest local minimum. Using the relaxed energies as the criteria of fitness, a fraction of the popula tion is selected as “parents.” The next generation of candidate structures is produced by “mating” these parents. The process is repeated until the ground state structure is located. APPLICATIONS OF GENERIC ALGORITHM Genetic algorithms are a very effective way of quickly finding a reasonable solution to a complex problem. Granted they aren’t insta ntaneous, or even close, but they do an excellent job of searching through a large and complex search space. Genetic algorithms are most effective in a search space for which little is known. You may know exactly what you want a solution to do but have no idea how you want it to go about doing it. This is where genetic algorithms th rive. They produce solutions that solve the problem in ways you may never have even considered. Then again, they can also produce solutions that only work within the test environment an d flounder once you try to use them in the real world. Put simply: use genetic algorithms for everything you cannot easily do with another algorithm. Originally defined by the crea tor John Holland, the goal of genetic algorithm was to abstract and rigorously explain the adaptive processes of natural systems and to develop ways in which natural adaptation might aid computer systems and software. This lead to important discoveries in both the natural and artificial realms. Smart card is just one aspect of a whole smart ID A smart card is essentially a card or token comprising a secure m icro processor core and non volatil e memory. It is like a visiting card, with a tiny computer chip inside it. It can store information and communicate with smart card termi nals that can read and write that information to the card, as well as act as interface between smart card and larger network. It is highly secured. At a bas ic level, smart card can perform four functions like data storage, identification, authentication and appli cation processing. In an ideal situation, a person can carry just a single smart card that acts as a credit card, cash card, ration card, PAN card, voter's card and eve n a passport. In this, security is in the form of PIN number, passwords etc... w ith sophisticated hacking equipmen t, smart card data can be interce pted. A secure processor core, on the other hand, would be tamper-resistant and prevent retrieval or modification of onchip physical attacks. The functioning of the smart card, especially the communication task requires power. The mode of performing the power giving process depends largely on the functions perfo rmed and the power required for these. Smart ID's are promising to he lp Shashank Shekhar Singh EC - II year government agencies tackle the se upcoming challenges better. As the technology reasonably matures, this is indeed the right time to in troduce smart ID's. 27 Electronics Spectra, 2010
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