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000827023 019__ $$a1028979413$$a1029208411$$a1029242242$$a1029322005$$a1034585824
000827023 020__ $$a9783319708515$$q(electronic book)
000827023 020__ $$a3319708511$$q(electronic book)
000827023 020__ $$z9783319708508
000827023 020__ $$z3319708503
000827023 0247_ $$a10.1007/978-3-319-70851-5$$2doi
000827023 035__ $$aSP(OCoLC)on1029091355
000827023 035__ $$aSP(OCoLC)1029091355$$z(OCoLC)1028979413$$z(OCoLC)1029208411$$z(OCoLC)1029242242$$z(OCoLC)1029322005$$z(OCoLC)1034585824
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000827023 049__ $$aISEA
000827023 050_4 $$aQ342
000827023 08204 $$a006.3$$223
000827023 1001_ $$aOlivas, Frumen,$$eauthor.
000827023 24510 $$aDynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic /$$cby Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin.
000827023 264_1 $$aCham :$$bSpringer International Publishing :$$bImprint: Springer,$$c2018.
000827023 300__ $$a1 online resource (vii, 105 pages) :$$billustrations.
000827023 336__ $$atext$$btxt$$2rdacontent
000827023 337__ $$acomputer$$bc$$2rdamedia
000827023 338__ $$aonline resource$$bcr$$2rdacarrier
000827023 347__ $$atext file$$bPDF$$2rda
000827023 4901_ $$aSpringerBriefs in applied sciences and technology,$$x2191-530X
000827023 504__ $$aIncludes bibliographical references and index.
000827023 5050_ $$aIntroduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.
000827023 506__ $$aAccess limited to authorized users.
000827023 520__ $$aIn this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment.
000827023 650_0 $$aEngineering.
000827023 650_0 $$aArtificial intelligence.
000827023 650_0 $$aComputational intelligence.
000827023 7001_ $$aValdez, Fevrier,$$eauthor.
000827023 7001_ $$aCastillo, Oscar,$$eauthor.
000827023 7001_ $$aMelin, Patricia,$$d1962-$$eauthor.
000827023 77608 $$iPrint version: $$z9783319708508
000827023 830_0 $$aSpringerBriefs in applied sciences and technology.
000827023 852__ $$bebk
000827023 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-70851-5$$zOnline Access$$91397441.1
000827023 909CO $$ooai:library.usi.edu:827023$$pGLOBAL_SET
000827023 980__ $$aEBOOK
000827023 980__ $$aBIB
000827023 982__ $$aEbook
000827023 983__ $$aOnline
000827023 994__ $$a92$$bISE