Category: Genetic Programming and Evolvable Machines

Acknowledgment

Acknowledgment
Content Type Journal ArticleDOI 10.1007/s10710-008-9078-6Authors
Lee Spector, Hampshire College School of Cognitive Science Amherst MA 01002 USA

Journal Genetic Programming and Evolvable MachinesOnline ISSN 1573-7632Print ISSN …

Acknowledgment

  • Content Type Journal Article
  • DOI 10.1007/s10710-008-9078-6
  • Authors
    • Lee Spector, Hampshire College School of Cognitive Science Amherst MA 01002 USA

Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

Abstract  Bloat is an excess of code growth without a corresponding improvement in fitness. This is a serious problem in Genetic Programming,
often leading to the stagnation of the evolutionary process. Here we provide an extensive review of…

Abstract  Bloat is an excess of code growth without a corresponding improvement in fitness. This is a serious problem in Genetic Programming,
often leading to the stagnation of the evolutionary process. Here we provide an extensive review of all the past and current
theories regarding why bloat occurs. After more than 15 years of intense research, recent work is shedding new light on what
may be the real reasons for the bloat phenomenon. We then introduce Dynamic Limits, our new approach to bloat control. It
implements a dynamic limit that can be raised or lowered, depending on the best solution found so far, and can be applied
either to the depth or size of the programs being evolved. Four problems were used as a benchmark to study the efficiency
of Dynamic Limits. The quality of the results is highly dependent on the type of limit used: depth or size. The depth variants
performed very well across the set of problems studied, achieving similar fitness to the baseline technique while using significantly
smaller trees. Unlike many other methods available so far, Dynamic Limits does not require specific genetic operators, modifications
in fitness evaluation or different selection schemes, nor does it add any parameters to the search process. Furthermore, its
implementation is simple and its efficiency does not rely on the usage of a static upper limit. The results are discussed
in the context of the newest bloat theory.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9075-9
  • Authors
    • Sara Silva, CISUC, University of Coimbra, Polo II – Pinhal de Marrocos 3030-290 Coimbra Portugal
    • Ernesto Costa, CISUC, University of Coimbra, Polo II – Pinhal de Marrocos 3030-290 Coimbra Portugal

Editorial introduction

Editorial introduction
Content Type Journal ArticleCategory EditorialDOI 10.1007/s10710-008-9077-7Authors
Lee Spector, Hampshire College School of Cognitive Science Amherst MA 01002 USA

Journal Genetic Programming and Evolvable MachinesOnline…

Editorial introduction

  • Content Type Journal Article
  • Category Editorial
  • DOI 10.1007/s10710-008-9077-7
  • Authors
    • Lee Spector, Hampshire College School of Cognitive Science Amherst MA 01002 USA

Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

Abstract  There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing
classification problems in the context of medical applications. As we show in this paper, the application…

Abstract  There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing
classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary
computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those
trained by standard machine learning methods. On the basis of five medical benchmark classification problems taken from the
UCI repository as well as the Melanoma data set (prepared by members of the Department of Dermatology of the Medical University Vienna) we document that the enhanced
genetic programming approach presented here is able to produce comparable or even better results than linear modeling methods,
artificial neural networks, kNN classification, support vector machines and also various genetic programming approaches.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9076-8
  • Authors
    • Stephan M. Winkler, Research Center Hagenberg, Upper Austria University of Applied Sciences Softwarepark 11 4232 Hagenberg Austria
    • Michael Affenzeller, Upper Austria University of Applied Sciences Department of Software Engineering Softwarepark 11 4232 Hagenberg Austria
    • Stefan Wagner, Upper Austria University of Applied Sciences Department of Software Engineering Softwarepark 11 4232 Hagenberg Austria

Incorporating characteristics of human creativity into an evolutionary art algorithm

Abstract  A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects
the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how c…

Abstract  A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects
the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated
art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in
this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The
goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just
produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity,
change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual
network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9074-x
  • Authors
    • Steve DiPaola, Simon Fraser University Surrey BC Canada
    • Liane Gabora, University of British Columbia Kelowna BC Canada

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9073-yAuthors
Michael O’Neill, School of Computer Science & Informatics, University …

Riccardo Poli, William B. Langdon, Nicholas F. McPhee: A Field Guide to Genetic Programming

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9073-y
  • Authors
    • Michael O’Neill, School of Computer Science & Informatics, University College Dublin Natural Computing Research & Applications Group, Complex and Adaptive Systems Laboratory Dublin Ireland

Solution of matrix Riccati differential equation for nonlinear singular system using genetic programming

Abstract  In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear
singular systems using genetic programming (GP). The goal is to provide optimal control with reduced c…

Abstract  In this paper, we propose a novel approach to find the solution of the matrix Riccati differential equation (MRDE) for nonlinear
singular systems using genetic programming (GP). The goal is to provide optimal control with reduced calculation effort by
comparing the solutions of the MRDE obtained from the well known traditional Runge Kutta (RK) method to those obtained from
the GP method. We show that the GP approach to the problem is qualitatively better

in terms of accuracy. Numerical examples are provided to illustrate the proposed method.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9072-z
  • Authors
    • P. Balasubramaniam, Gandhigram Rural University Department of Mathematics Gandhigram 624 302 Tamilnadu India
    • A. Vincent Antony Kumar, PSNA College of Engineering and Technology Department of Computer Science and Applications Dindigul 624 622 Tamilnadu India

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9071-0Authors
Jeroen Eggermont, Leiden University Medical Center…

Juan Romero and Penousal Machado (eds): The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9071-0
  • Authors
    • Jeroen Eggermont, Leiden University Medical Center Division of Image Processing, Department of Radiology Leiden The Netherlands

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”
Content Type Journal ArticleCategory Book ReviewDOI 10.1007/s10710-008-9070-1Authors
Pierre Collet, Universite de Strasbourg FDBT-LSIIT Strasbourg France
…

Husbands, Holland, and Wheeler (eds): Review of the book “The Mechanical Mind in History”

  • Content Type Journal Article
  • Category Book Review
  • DOI 10.1007/s10710-008-9070-1
  • Authors
    • Pierre Collet, Universite de Strasbourg FDBT-LSIIT Strasbourg France

An improved representation for evolving programs

Abstract  A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining
their advantages. This combines the easy reproduction of the linear representation with the inherita…

Abstract  A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining
their advantages. This combines the easy reproduction of the linear representation with the inheritable characteristics of
the tree representation by using fixed-length blocks of genes representing single program statements. This means that each
block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes
to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a
clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions
was evolved as an application of the new Genetic Program components. These functions have the common feature that they all
need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation
problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic
increase in the required evolution time.

  • Content Type Journal Article
  • Category Original Paper
  • DOI 10.1007/s10710-008-9069-7
  • Authors
    • M. S. Withall, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK
    • C. J. Hinde, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK
    • R. G. Stone, Loughborough University Department of Computer Science Loughborough, Leics LE11 3TU England, UK