LEADER 04084nam 22006255 450 001 9910299702903321 005 20200701130505.0 010 $a81-322-1958-9 024 7 $a10.1007/978-81-322-1958-3 035 $a(CKB)3710000000224693 035 $a(EBL)1802682 035 $a(SSID)ssj0001338534 035 $a(PQKBManifestationID)11769122 035 $a(PQKBTitleCode)TC0001338534 035 $a(PQKBWorkID)11338290 035 $a(PQKB)11384585 035 $a(DE-He213)978-81-322-1958-3 035 $a(MiAaPQ)EBC1802682 035 $a(PPN)180622544 035 $a(EXLCZ)993710000000224693 100 $a20140820d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplication of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems$b[electronic resource] /$fedited by M.C. Bhuvaneswari 205 $a1st ed. 2015. 210 1$aNew Delhi :$cSpringer India :$cImprint: Springer,$d2015. 215 $a1 online resource (181 p.) 300 $aDescription based upon print version of record. 311 $a1-322-17396-6 311 $a81-322-1957-0 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction to Multi-Objective Evolutionary Algorithms -- Hardware/Software Partitioning for Embedded Systems -- Circuit Partitioning for VLSI Layout -- Design of Operational Amplifier -- Design Space Exploration for Scheduling and Allocation in High Level Synthesis of Datapaths -- Design Space Exploration of Datapath (Architecture) in High Level Synthesis for Computation Intensive Applications -- Design Flow from Algorithm to RTL using Evolutionary Exploration Approach -- Crosstalk Delay Fault Test Generation -- Scheduling in Heterogeneous Distributed Systems.  . 330 $aThis book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field of VLSI and embedded system design. The book introduces multi-objective GA and PSO in a simple and easily understandable way that will appeal to introductory readers. 606 $aElectronic circuits 606 $aComputational intelligence 606 $aMathematical optimization 606 $aCircuits and Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/T24068 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 615 0$aElectronic circuits. 615 0$aComputational intelligence. 615 0$aMathematical optimization. 615 14$aCircuits and Systems. 615 24$aComputational Intelligence. 615 24$aOptimization. 676 $a006.3 676 $a621.39/5 702 $aBhuvaneswari$b M.C$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299702903321 996 $aApplication of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems$91412970 997 $aUNINA