A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
HYPER-LP: a system for power minimization using architectural transformations
ICCAD '92 1992 IEEE/ACM international conference proceedings on Computer-aided design
Technology decomposition and mapping targeting low power dissipation
DAC '93 Proceedings of the 30th international Design Automation Conference
Multi-level network optimization for low power
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
Power analysis of embedded software: a first step towards software power minimization
ICCAD '94 Proceedings of the 1994 IEEE/ACM international conference on Computer-aided design
A survey of optimization techniques targeting low power VLSI circuits
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Transistor reordering rules for power reduction in CMOS gates
ASP-DAC '95 Proceedings of the 1995 Asia and South Pacific Design Automation Conference
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Using complementation and resequencing to minimize transitions
DAC '98 Proceedings of the 35th annual Design Automation Conference
Genetic algorithms for VLSI design, layout & test automation
Genetic algorithms for VLSI design, layout & test automation
Evolutionary algorithms for VLSI CAD
Evolutionary algorithms for VLSI CAD
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Instruction Level Power Analysis and Optimization of Software
VLSID '96 Proceedings of the 9th International Conference on VLSI Design: VLSI in Mobile Communication
Evolutionary Algorithms for Embedded System Design
Evolutionary Algorithms for Embedded System Design
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Optimal waveband switching in optical ring networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Optimal Wavebanding in WDM Ring Networks
IEEE/ACM Transactions on Networking (TON)
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An important aim of circuit design is the reduction of the power dissipation. Power consumption of digital circuits is closely related to switching activity. Due to the increase in the usage of battery driven devices (e.g. PDAs, laptops), the low power aspect became one of the main issues in circuit design in recent years. In this context, the Data Ordering Problem with and without Inversion is very important. Data words have to be ordered and (eventually) negated in order to minimize the total number of bit transitions. These problems have several applications, like instruction scheduling, compiler optimization, sequencing of test patterns, or cache write-back. This paper describes two evolutionary algorithms for the Data Ordering Problem with Inversion (DOPI). The first one sensibly improves the Greedy Min solution (the best known related polynomial heuristic) by a small amount of time, by successively applying mutation operators. The second one is a hybrid genetic algorithm, where a part of the population is initialized using greedy techniques. Greedy Min and Lower Bound algorithms are used for verifying the performance of the presented Evolutionary Algorithms (EAs) on a large set of experiments. A comparison of our results to previous approaches proves the efficiency of our second approach. It is able to cope with data sets which are much larger than those handled by the best known EAs. This improvement comes from the synchronized strategy of applying the genetic operators (algorithm design) as well as from the compact representation of the data (algorithm implementation).