An evolutionary computation approach to the software engineering of evolving programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "George Smith Cowan",
  title =        "An evolutionary computation approach to the software
                 engineering of evolving programs",
  school =       "Computer Science, Wayne State University",
  year =         "1999",
  address =      "Detroit, Michigan, USA",
  keywords =     "genetic algorithms, genetic programming, Cultural
                 Algorithm, Software Engineering Environment for
                 Evolving Programs, SWEEP, Discipulus",
  URL =          "",
  URL =          "",
  size =         "196 pages",
  abstract =     "The Software Engineering Environment for Evolving
                 Programs (SWEEP) is a prototype for an evolving
                 software development environment consisting of adaptive
                 agents. SWEEP captures the salient features of
                 automated assistance for the software systems of the
                 future: distributed agents specializing in different
                 life-cycle activities, focusing on software evolution,
                 with an adaptive capability that will allow the agents
                 to evolve new development processes. The programming
                 agents for this system are Genetic Programming (GP)
                 elements. A common architectural scheme for the agents,
                 Cultural Algorithms (CA), facilitates the learning and
                 interaction between the agents. The processes used by
                 the GP agents create programs that are quite different
                 from those of traditional processes. Two specific
                 differences introduced with the use of the GP process
                 are the phenomenon of bloat and the software quality
                 issue of solution program generalizability to new
                 problem instances. Unnecessary complexity compromises
                 program generalization, but bloat obscures this
                 relationship by introducing extra structural complexity
                 into the program without affecting the program's
                 outputs. Three levels of bloat are characterized: local
                 bloat, global bloat , and representational

                 Hopefully, any new assessment processes for new quality
                 issues and new code phenomena can build on traditional
                 assessment knowledge. The Metrics Apprentice (MA) is a
                 prototype knowledge-based Software Quality Agent that
                 constructs new software metrics, based on traditional
                 software engineering metrics, for assessing software
                 quality issues. To facilitate the learning of new
                 concepts, the MA uses a CA shell that combines an
                 evolutionary programming population component with a
                 semantic network of schemata, or generalized knowledge,
                 in a belief space. The performance of the system for a
                 symbolic regression problem was compared to that of a
                 traditional linear discriminant analysis (LDA)
                 statistical approach. As more bloat was removed, the
                 knowledge-based MA was able to outperform the LDA
                 approach through the emergence of a hierarchical
                 structure in its belief space. This structure allowed
                 the MA knowledge-based approach to climb the conceptual
                 hierarchy to use traditional software metrics that had
                 a higher knowledge level, such as Intelligence Content,
                 leading to an increased ability to predict the
                 Generalizability of the GP produced code.",
  notes =        "
                 Supervisor: Robert Gene Reynolds

                 McCabe's Cyclomatic Complexity measurements.

                 Chapter 7 Relationship of Software Metrics to Bloat.
                 Chapter 8 Defining a New Software Metric To Estimate
                 Generalization Using the Metrics Apprentice UMI
                 Microform 9956975",

Genetic Programming entries for George S Cowan