Parallel and Distributed Bioinspired Algorithms

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        Bioinspired algorithms are nowadays routinely applied for solving hard
      
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        and large problems in a variety of areas. Over the years, researchers
      
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        have incorporated different forms of arallelism into these algorithms
      
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        resulting in significant efficiency gains and the solution of even
      
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        larger and harder problems. The advent of Computational Grids has
      
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        enhanced our capabilities to deploy more complex systems and is
      
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        allowing new improvements that extend the original conception and
      
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        execution of bioinspired serial algorithms.
      
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<p>

      
        This Special Issue will collect state-of-the-art research on all
      
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        aspects of Parallel and Distributed Bioinspired Algorithms. The topics
      
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        of interest include (but are not limited to):
      
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        · Distributed Evolutionary Algorithms (PEAs), including Genetic
      
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        Algorithms, Genetic
      
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        Programming, Evolutionary Programming, and Evolution Strategies.
      
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        · Other biologically inspired parallel and distributed algorithms: Ant
      
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        Colonies, Immune Systems, Artificial Life, Cultural Algorithms,
      
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        Swarms, etc.
      
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        · Analytical modeling of Parallel Bioinspired Algorithms.
      
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        · Cluster and Grid implementation of Bioinspired Algorithms.
      
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        · Fault tolerant implementations.
      
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        · Performance evaluations.
      
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        · Improvement in system performance through optimization and tuning.
      
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        · Case studies showing the role of Distributed Parallel Bioinspired
      
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        Algorithms when solving hard real-life problems.
      
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