Meta-heuristic algorithms for FPGA segmented channel routing problems with non-standard cost functions


Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)


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. © 2005 Springer Science + Business Media, Inc.
Original languageEnglish
Pages (from-to)359-379
Number of pages21
JournalGenetic Programming and Evolvable Machines
Issue number4
Early online date16 Aug 2005
Publication statusPublished - Dec 2005
Externally publishedYes

Bibliographical note

This work has been partially supported by a research project of the Universidad de Alcalá, project number UAH PI2005/078.


  • FPGAs
  • Genetic algorithms
  • Hybrid algorithms
  • Segmented channel architecture
  • Simulated annealing


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