Research Output

State assignment for sequential circuits using multi-objective genetic algorithm.

  In this study, a new approach using a multi-objective genetic algorithm (MOGA) is proposed to determine the optimal state assignment with less area and power dissipations for completely and incompletely specified sequential circuits. The goal is to find the best assignments which reduce the component count and switching activity. The MOGA employs a Pareto ranking scheme and produces a set of state assignments, which are optimal in both objectives. The ESPRESSO tool is used to optimise the combinational parts of the sequential circuits. Experimental results are given using a personal computer with an Intel CPU of 2.4 GHz and 2 GB RAM. The algorithm is implemented using C++ and fully tested with benchmark examples. The experimental results show that saving in components and switching activity are achieved in most of the benchmarks tested compared with recent published research.

  • Type:


  • Date:

    30 June 2011

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  • Publisher


  • DOI:


  • ISSN:


  • Library of Congress:

    TK Electrical engineering. Electronics Nuclear engineering

  • Dewey Decimal Classification:

    621.3 Electrical & electronic engineering


Al-Jassani, B. A., Urquhart, N. B. & Almaini, A. E. A. (2011). State assignment for sequential circuits using multi-objective genetic algorithm. IET Computers and Digital Techniques. 5, 296-305. doi:10.1049/iet-cdt.2010.0045. ISSN 1751-8601



multiobjective genetic algorithm; component count; switching activity; Pareto ranking scheme; state assignments; ESPRESSO tool; combinational parts; power dissipations; incompletely specified sequential circuits; completely specified sequential circuits

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