HEA|  |03288cam  2200313   450 
001|  |022022007179
008|  |210601s2021    sz            000 0 eng d
020|  |▼a9783030665142▼cCNY795.44▼qhardcover
020|  |▼a3030665143▼qhardcover
020|  |▼z9783030665159▼qelectronic bk.
020|  |▼z3030665151▼qelectronic bk.
040|  |▼aYDX▼beng▼erda▼cYDX▼dGW5XE▼dO-
   |  |CLCO▼dEBLCP
082|04|▼a006.3/10151▼223
090|  |▼aTP181▼b227430
245|00|▼aBlack box optimization, mach-
   |  |ine learning, and no-free lunc-
   |  |h theorems /▼cPanos M. Pardalo-
   |  |s, Varvara Rasskazova, Michael-
   |  | N. Vrahatis, editors.
260| 1|▼aCham :▼bSpringer,▼c2021.
300|  |▼a398 pages ;▼c24 cm.
336|  |▼atext▼btxt▼2rdacontent
337|  |▼aunmediated▼bn▼2rdamedia
338|  |▼avolume▼bnc▼2rdacarrier
490|0 |▼aSpringer optimization and it-
   |  |s applications,▼x1931-6828 ;▼vvolume 170
505|0 |▼aLearning enabled constrained-
   |  | black box optimization (Arche-
   |  |tti) －－ Black-box optimization-
   |  |: Methods and applications (Ha-
   |  |san) －－ Tuning algorithms for -
   |  |stochastic black-box optimizat-
   |  |ion: State of the art and futu-
   |  |re perspectives (Bartz-Beielst-
   |  |ein) －－ Quality diversity opti-
   |  |mization: A novel branch of st-
   |  |ochastic optimization (Chatzil-
   |  |ygeroudis) －－ Multi-objective -
   |  |evolutionary algorithms: Past,-
   |  | present and future (Coello C.-
   |  |A) －－ Black-box and data drive-
   |  |n computation (Du) －－ Mathemat-
   |  |ically rigorous global optimiz-
   |  |ation and fuzzy optimization: -
   |  |A brief comparison of paradigm-
   |  |s, methods, similarities and d-
   |  |ifferences (Kearfott) －－ Optim-
   |  |ization under Uncertainty Expl-
   |  |ains Empirical Success of Deep-
   |  | Learning Heuristics (Kreinovi-
   |  |ch) －－ Variable neighborhood p-
   |  |rogramming as a tool of machin-
   |  |e learning (Mladenovic) －－ Non-
   |  |-lattice covering and quanitiz-
   |  |ation of high dimensional sets-
   |  | (Zhigljavsky) －－ Finding effe-
   |  |ctive SAT partitionings via bl-
   |  |ack-box optimization (Semenov)-
   |  | －－ The No Free Lunch Theorem:-
   |  | What are its main implication-
   |  |s for the optimization practic-
   |  |e? ( Serafino) －－ What is impo-
   |  |rtant about the No Free Lunch -
   |  |theorems? (Wolpert)
520|  |▼aThis edited volume illustrat-
   |  |es the connections between mac-
   |  |hine learning techniques, blac-
   |  |k box optimization, and no-fre-
   |  |e lunch theorems. Each of the -
   |  |thirteen contributions focuses-
   |  | on the commonality and interd-
   |  |isciplinary concepts as well a-
   |  |s the fundamentals needed to f-
   |  |ully comprehend the impact of -
   |  |individual applications and pr-
   |  |oblems. Current theoretical, a-
   |  |lgorithmic, and practical meth-
   |  |ods used are provided to stimu-
   |  |late a new effort towards inno-
   |  |vative and efficient solutions-
   |  |. The book is intended for beg-
   |  |inners who wish to achieve a b-
   |  |road overview of optimization -
   |  |methods and also for more expe-
   |  |rienced researchers as well as-
   |  | researchers in mathematics, o-
   |  |ptimization, operations resear-
   |  |ch, quantitative logistics, da-
   |  |ta analysis, and statistics, w-
   |  |ho will benefit from access to-
   |  | a quick reference to key topi-
   |  |cs and methods. The coverage r-
   |  |anges from mathematically rigo-
   |  |rous methods to heuristic and -
   |  |evolutionary approaches in an -
   |  |attempt to equip the reader wi-
   |  |th different viewpoints of the-
   |  | same problem.
650| 0|▼aMachine learning▼xMathematics.
650| 0|▼aMathematical optimization.
650| 0|▼aComputer algorithms.
700|1 |▼aPardalos, P. M.▼q(Panos M.),-
   |  |▼d1954-▼eeditor.
700|1 |▼aRasskazova, Varvara,▼eeditor.
700|1 |▼aVrahatis, Michael N.,▼d1955-▼eeditor.