Genetic Programming and Evolvable Machines

, Volume 6, Issue 4, pp 359–379 | Cite as

Meta-Heuristic Algorithms for FPGA Segmented Channel Routing Problems with Non-standard Cost Functions

  • Sancho Salcedo-SanzEmail author
  • Yong Xu
  • Xin Yao


In this paper we present three meta-heuristic approaches for FPGA segmented channel routing problems (FSCRPs) with a new cost function in which the cost of each assignment is not known in advance, and the cost of a solution only can be obtained from entire feasible assignments. Previous approaches to FSCPs cannot be applied to this kind of cost functions, and meta-heuristics are a good option to tackle the problem. We present two hybrid algorithms which use a Hopfield neural network to solve the problem's constraints, mixed with a Genetic Algorithm (GA) and a Simulated Annealing (SA). The third approach is a GA which manages the problem's constraints with a penalty function. We provide a complete analysis of the three metaheuristics, by tested them in several FSCRP instances, and comparing their performance and suitability to solve the FSCRP.


FPGAs segmented channel architecture hybrid algorithms genetic algorithms simulated annealing 


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Departamento de Teoría de la Señal y ComunicacionesUniversidad de AlcaláSpain
  2. 2.School of Computer ScienceThe University of BirminghamUK

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