000 04317nam a22005655i 4500
001 978-3-662-62007-6
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008 200816s2020 gw | s |||| 0|eng d
020 _a9783662620076
_9978-3-662-62007-6
024 7 _a10.1007/978-3-662-62007-6
_2doi
050 4 _aQA75.5-76.95
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
_2thema
082 0 4 _a004.0151
_223
100 1 _aLockett, Alan J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGeneral-Purpose Optimization Through Information Maximization
_h[electronic resource] /
_cby Alan J. Lockett.
250 _a1st ed. 2020.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2020.
300 _aXVIII, 561 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x2627-6461
505 0 _aIntroduction -- Review of Optimization Methods -- Functional Analysis of Optimization -- A Unified View of Population-Based Optimizers -- Continuity of Optimizers -- The Optimization Process -- Performance Analysis -- Performance Experiments -- No Free Lunch Does Not Prevent General Optimization -- The Geometry of Optimization and the Optimization Game -- The Evolutionary Annealing Method -- Evolutionary Annealing In Euclidean Space -- Neuroannealing -- Discussion and Future Work -- Conclusion -- App. A, Performance Experiment Results -- App. B, Automated Currency Exchange Trading. .
520 _aThis book examines the mismatch between discrete programs, which lie at the center of modern applied mathematics, and the continuous space phenomena they simulate. The author considers whether we can imagine continuous spaces of programs, and asks what the structure of such spaces would be and how they would be constituted. He proposes a functional analysis of program spaces focused through the lens of iterative optimization. The author begins with the observation that optimization methods such as Genetic Algorithms, Evolution Strategies, and Particle Swarm Optimization can be analyzed as Estimation of Distributions Algorithms (EDAs) in that they can be formulated as conditional probability distributions. The probabilities themselves are mathematical objects that can be compared and operated on, and thus many methods in Evolutionary Computation can be placed in a shared vector space and analyzed using techniques of functional analysis. The core ideas of this book expand from that concept, eventually incorporating all iterative stochastic search methods, including gradient-based methods. Inspired by work on Randomized Search Heuristics, the author covers all iterative optimization methods and not just evolutionary methods. The No Free Lunch Theorem is viewed as a useful introduction to the broader field of analysis that comes from developing a shared mathematical space for optimization algorithms. The author brings in intuitions from several branches of mathematics such as topology, probability theory, and stochastic processes and provides substantial background material to make the work as self-contained as possible. The book will be valuable for researchers in the areas of global optimization, machine learning, evolutionary theory, and control theory.
650 0 _aComputer science.
650 0 _aArtificial intelligence.
650 0 _aMathematical optimization.
650 0 _aComputer science
_xMathematics.
650 1 4 _aTheory of Computation.
650 2 4 _aArtificial Intelligence.
650 2 4 _aOptimization.
650 2 4 _aMathematics of Computing.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783662620069
776 0 8 _iPrinted edition:
_z9783662620083
776 0 8 _iPrinted edition:
_z9783662620090
830 0 _aNatural Computing Series,
_x2627-6461
856 4 0 _uhttps://doi.org/10.1007/978-3-662-62007-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175009
_d175009