Designing Effective Evolutionary Computations

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Chellapilla:thesis,
  author =       "Kumar H. Chellapilla",
  title =        "Designing Effective Evolutionary Computations",
  school =       "Electrical Engineering, University of California, San
                 Diego",
  year =         "2005",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9780542572180",
  URL =          "http://phdtree.org/pdf/26271964-designing-effective-evolutionary-computations/",
  URL =          "http://search.proquest.com/docview/305003505",
  size =         "606 pages",
  abstract =     "Evolutionary algorithms offer a practical approach to
                 solving difficult real-world problems. In many problem
                 domains, these are the only possible approaches with
                 potential for effectively searching through complex
                 solution spaces. For novel problem domains wherein
                 previous research efforts are sparse or problem domain
                 expertise is in its infancy, evolutionary algorithms
                 offer strong alternatives for exploring the solution
                 spaces and also gaining insights into effectively
                 solving the problem.

                 The principal roadblock in conventional practice is the
                 lack of a specific approach which permits one to
                 simultaneously control an algorithm's representation,
                 population variation operators and population selection
                 operators. An approach based on mathematically sound
                 principles is adopted in this thesis to provide
                 asymptotic guarantees on evolutionary algorithm
                 performance followed by useful real-time methods for
                 improving the rate of convergence. In particular, the
                 evolutionary algorithm is decomposed into its
                 constituent representation, population variation, and
                 population selection operators. The population
                 variation operators are further broken down into
                 solution variation operators. Each component is
                 independently analysed without being constrained by an
                 overall architecture for the evolutionary
                 algorithm.

                 Each component presents several alternatives that can
                 be chosen independently to control desired properties
                 of the evolutionary algorithm. A new mathematical model
                 for analysing evolutionary algorithms is developed, and
                 necessary and sufficient conditions on the variation
                 and selection operators for asymptotic convergence are
                 derived. Fitness distributions and fitness distribution
                 feature based heuristics are presented to improve the
                 rate of convergence of an evolutionary algorithm. This
                 thesis also presents a wide array of empirical results
                 to demonstrate the utility, effectiveness, and
                 applicability of the new theory. Within the new
                 framework, evolutionary algorithms are applied to solve
                 real, discrete and mixed parameter optimization
                 problems. Evolutionary algorithms that guarantee
                 asymptotic convergence are designed to solve problems
                 involving structures such as parse trees and finite
                 state machines. Co-evolutionary algorithms are designed
                 to evolve an expert checkers player that rated 2045
                 against human checkers players. Fitness distribution
                 heuristics are used to tune an evolutionary algorithm
                 for improved rate of convergence for solving the
                 travelling salesman problem.",
  notes =        "Supervisor Anthony Sebald

                 UMI Microform 3208643",
}

Genetic Programming entries for Kumar Chellapilla

Citations