- author = "Antonello Dessi",
- title = "Automatic subroutine discovery in Genetic Programming (Text In Italian)",
- title_italian = "Scoperta automatica di subroutine in Programmazione Genetica",
- school = "University of Pisa",
- year = "1998",
- type = "tesi di laurea",
- address = "Italy",
- keywords = "genetic algorithms, genetic programming, subroutines, ma, arl, aao",
-
URL = "
http://web.mclink.it/MC2657/tesi.html",
-
URL = "http://web.mclink.it/MC2657/file/tesi.zip",
- size = "292 pages",
-
abstract = "May 2018 Note this was translated from Italian by
Google Translate..... The developments of Artificial
Intelligence have shown that in dealing with complex
problems the key to success is the ability to break
them down into simpler subproblems, creating a
hierarchical and modular structure that allows to reach
a level of difficulty that can be faced. Whether you
consider Genetic Programming as an effective
possibility to reach a completely automatic programming
in the future, whether we consider it only an
alternative method for searching for algorithmic
solutions for a wide range of problems, the importance
of the management of the subroutines is therefore
fundamental. The Genetic Programming in its original
version fails to implement a real process of
hierarchical decomposition of the problems, but through
the introduction of the automatic discovery of the
subroutine this goal is achieved. The primary purpose
of the work carried out is therefore the analysis of
how this mechanism can be efficiently implemented, of
what the actual performances are observed, and which
are the possibilities for improvement.
The first chapter presents the Genetic Programming framing it within the Evolutionary Algorithms , illustrating the numerous variants present in the literature, the current research directions and the possible applications of the model.
The second chapter illustrates the theoretical aspects of Genetic Programming, with particular attention to the theorems that try to model their behaviour (Scheme and Price Theorem), highlighting their intrinsic limits.
The third chapter analyses the different possibilities concerning the introduction of subroutines in Genetic Programming, to arrive at identifying the ARL model as more promising . The various heuristics for the automatic discovery of the subroutines are then reviewed, and then proposed a new one ( salience ). Finally the ARL algorithm is reviewed, criticizing some aspects and proposing some variants, such as the use of the mutation for the diffusion of the subroutines ( diffusion by mutation)) and an alternative method for the dynamic era ( Maxfit ).
The fourth chapter illustrates the extensive experimental analysis carried out, divided into three phases: the first concerns the direct comparison between the selection heuristics of the subroutines, the second evaluates the effectiveness of the automatic addition of the arguments to the new subroutines, the third analyses the problem when it is appropriate to insert new subroutines in the program population ( dynamic era). In this way the fundamental aspects of the ARL algorithm are analysed and the efficacy of the proposals advanced in the thesis: at the end of the chapter are reported the relative conclusions and the possible directions of future research.
The appendix contains further experimental data and the source of the program developed specifically for the needs of the thesis.",
-
notes = "In Italian
Includes source code in C. The C code has elements of Module Acquisition (MA) and Adaptive Representation with Learning (ARL)
relatori Antonina Starita and Antonella Giani",
