Competent Algorithms for Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{PawlakPhD2015,
  author =       "Tomasz P. Pawlak",
  title =        "Competent Algorithms for Geometric Semantic Genetic
                 Programming",
  school =       "Poznan University of Technology",
  year =         "2015",
  address =      "Poznan, Poland",
  month =        "21 " # sep,
  keywords =     "genetic algorithms, genetic programming, semantics,
                 semantic backpropagation, effectiveness, neutrality,
                 geometry, theory, experiment, ECJ",
  URL =          "http://www.cs.put.poznan.pl/tpawlak/link/?PhD",
  URL =          "http://www.cs.put.poznan.pl/tpawlak/files/research/2015PhD/PhD%20Thesis.pdf",
  size =         "176 pages",
  abstract =     "Genetic Programming (GP) is a machine learning
                 technique for automatic induction of computer programs
                 from examples. The examples typically consist of two
                 parts: program arguments: the input and a target
                 program output. Both input and output may be expressed
                 in terms of numeric or textual variables or even a
                 conglomerate of the above. This problem formulation
                 enables formalising semantics of a program as a tuple
                 of outputs it returns in effect of execution on the
                 sample inputs. Use of semantics in GP gains an interest
                 in research community, since the semantic methods
                 developed so far in GP proved capable of achieving
                 lower error, better generalisation and smaller programs
                 and quicker convergence to an optimum than the
                 contemporary methods.

                 We embrace existing notions of semantics of program,
                 semantic distance, semantic neutrality and
                 effectiveness of genetic operators under the umbrella
                 of common conceptual framework for Semantic Genetic
                 Programming (SGP).

                 Then, we show that if a fitness function is a metric,
                 the fitness landscape spanned over a space of all
                 programs proxied by semantics becomes a cone with the
                 optimal semantics in the apex. This provides
                 justification for use of the recently developed
                 Geometric Semantic Genetic Programming (GSGP), where
                 geometric genetic operators use the conic shape of the
                 landscape. We derive properties of progress and
                 progress bounds of geometric operators for different
                 combinations of fitness functions and semantic
                 distances.

                 We present a comprehensive literature review of
                 existing semantic methods, discuss their advantages and
                 disadvantages and for each show how the defined
                 properties apply to it.

                 Next, we propose an algorithm for backpropagating
                 semantics trough program structure and competent
                 algorithms for operators: population initialization,
                 parent selection, mutation and crossover that are
                 approximately geometric, effective and free of certain
                 drawbacks of the existing geometric methods.

                 Then, we experimentally assess the proposed algorithms
                 and compare them with the existing methods in terms of
                 training set fitness, generalization on test set,
                 probability of performing geometric and effective
                 application, size of produced programs and
                 computational costs. We use a suite of nine symbolic
                 regression and nine Boolean program synthesis
                 benchmarks. The analysis shows that the proposed
                 algorithms achieve performance that is consistently
                 better than that offered by other semantic GP methods
                 for symbolic regression domain and not worse than the
                 best other methods for Boolean domain.

                 Finally, we experimentally find the proportions of
                 competent mutation and competent crossover that lead to
                 the optimal results in the above range of benchmarks.",
  notes =        "Supervisor: Krzysztof Krawiec

                 Supplementary material

                 The Java source code of Competent Algorithms for
                 Geometric Semantic Genetic Programming and other parts
                 of the experimental suite presented in the thesis,
                 together with the compiled Jar are available under
                 conditions of Academic Free License. Java 1.7+ is
                 required to compile the Jar from source. Instructions
                 how to create configuration files and run the
                 experiment are available in ECJ manual. The classes
                 under interest of this study can be found in the
                 following namespaces:

                 ec.app.semanticGP.* (problem definitions)
                 ec.gp.semantic.* (operators and basic semantic stuff)
                 library.* (semantic library helper classes)",
}

Genetic Programming entries for Tomasz Pawlak

Citations