Sustainable Evolutionary Algorithms and Scalable Evolutionary Synthesis of Dynamic Systems

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  author =       "Jianjun Hu",
  title =        "Sustainable Evolutionary Algorithms and Scalable
                 Evolutionary Synthesis of Dynamic Systems",
  school =       "Michigan State University",
  year =         "2004",
  address =      "East Lancing, Michigan, 48823, USA",
  month =        "18 " # aug,
  keywords =     "genetic algorithms, genetic programming, HFC",
  URL =          "",
  size =         "269 pages",
  abstract =     "This dissertation concerns the principles and
                 techniques for scalable evolutionary computation to
                 achieve better solutions for larger problems with more
                 computational resources. It suggests that many of the
                 limitations of existent evolutionary algorithms, such
                 as premature convergence, stagnation, loss of
                 diversity, lack of reliability and efficiency, are
                 derived from the fundamental convergent evolution
                 model, the oversimplified {"}survival of the fittest{"}
                 Darwinian evolution model. Within this model, the
                 higher the fitness the population achieves, the more
                 the search capability is lost. This is also the case
                 for many other conventional search techniques.

                 The main result of this dissertation is the
                 introduction of a novel sustainable evolution model,
                 the Hierarchical Fair Competition (HFC) model, and
                 corresponding five sustainable evolutionary algorithms
                 (EA) for evolutionary search. By maintaining
                 individuals in hierarchically organized fitness levels
                 and keeping evolution going at all fitness levels, HFC
                 transforms the conventional convergent evolutionary
                 computation model into a sustainable search framework
                 by ensuring a continuous supply and incorporation of
                 low-level building blocks and by culturing and
                 maintaining building blocks of intermediate levels with
                 its assembly-line structure. By reducing the selection
                 pressure within each fitness level while maintaining
                 the global selection pressure to help ensure
                 exploitation of good building blocks found, HFC
                 provides a good solution to the explore vs.
                 exploitation dilemma, which implies its wide
                 applications in other search, optimization, and machine
                 learning problems and algorithms.

                 The second theme of this dissertation is an examination
                 of the fundamental principles and related techniques
                 for achieving scalable evolutionary synthesis. It first
                 presents a survey of related research on principles for
                 handling complexity in artificially designed and
                 naturally evolved systems, including modularity, reuse,
                 development, and context evolution. Limitations of
                 current genetic programming based evolutionary
                 synthesis paradigm are discussed and future research
                 directions are outlined. Within this context, this
                 dissertation investigates two critical issues in
                 topologically open-ended evolutionary synthesis, using
                 bond-graph-based dynamic system synthesis as benchmark
                 problems. For the issue of balanced topology and
                 parameter search in evolutionary synthesis, an
                 effective technique named Structure Fitness Sharing
                 (SFS) is proposed to maintain topology search
                 capability. For the representation issue in
                 evolutionary synthesis, or more specifically the
                 function set design problem of genetic programming, two
                 modular set approaches are proposed to investigate the
                 relationship between representation, evolvability, and
  notes =        "Related research

Genetic Programming entries for Jianjun Hu