The route to a defect tolerant LUT through artificial evolution

Abstract  Evolutionary techniques may be applied to search for specific structures or functions, as specified in the fitness function.
This paper addresses the challenge of finding an appropriate fitness function when searching for generic r…

Abstract  

Evolutionary techniques may be applied to search for specific structures or functions, as specified in the fitness function.
This paper addresses the challenge of finding an appropriate fitness function when searching for generic rather than specific
structures which, when combined wiacteristic of defect tolerance on the circuit. Production defects for integrated circuits
are expected to increase considerably. To avoid a corresponding drop in yield, improved defect tolerance solutions are needed.
In the case of Field Programmable Gate Arrays (FPGAs), the pre-designed gate array provides a bridge between production and
the application designers. Thus, introduction of defect tolerant techniques to the FPGA itself could provide a defect free
gate array to the application designer, despite production defects. The search for defect tolerance presented herein is directed
at finding defect tolerant structures for an important building block of FPGAs: Look-Up Tables (LUTs). Two key approaches
are presented: (1) applying evolved generic building blocks to a traditional LUT design and (2) evolving the LUT design directly.
The results highlight the fact that evolved generic defect tolerant structures can contribute to highly reliable circuit designs
at the expense of area usage. Further, they show that applying such a technique, rather than direct evolution, has benefits
with respect to evolvability of larger circuits, again at the expense of area usage.

  • Content Type Journal Article
  • Pages 281-303
  • DOI 10.1007/s10710-011-9129-2
  • Authors
    • Asbjoern Djupdal, CRAB Lab, IDI, NTNU, Trondheim, Norway
    • Pauline C. Haddow, CRAB Lab, IDI, NTNU, Trondheim, Norway

Acknowledgment

Acknowledgment
Content Type Journal ArticlePages 3-4DOI 10.1007/s10710-010-9128-8Authors
Lee Spector, School of Cognitive Science, Hampshire College, Amherst, MA 01002, USA

Journal Genetic Programming and Evolvable MachinesOnline ISSN 1573-76…

Acknowledgment

  • Content Type Journal Article
  • Pages 3-4
  • DOI 10.1007/s10710-010-9128-8
  • Authors
    • Lee Spector, School of Cognitive Science, Hampshire College, Amherst, MA 01002, USA

Editorial introduction

Editorial introduction
Content Type Journal ArticlePages 1-2DOI 10.1007/s10710-010-9127-9Authors
Lee Spector, School of Cognitive Science, Hampshire College, Amherst, MA 01002, USA

Journal Genetic Programming and Evolvable MachinesOnline ISSN…

Editorial introduction

  • Content Type Journal Article
  • Pages 1-2
  • DOI 10.1007/s10710-010-9127-9
  • Authors
    • Lee Spector, School of Cognitive Science, Hampshire College, Amherst, MA 01002, USA

Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning

Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning
Content Type Journal ArticlePages 179-180DOI 10.1007/s10710-010-9126-xAuthors
Kalyan Veeramachaneni, Massachusetts Institute of Technology, Cambridge…

Hitoshi Iba, Topon Kumar Paul, Yoshohiko Hasegawa: Applied genetic programming and machine learning

  • Content Type Journal Article
  • Pages 179-180
  • DOI 10.1007/s10710-010-9126-x
  • Authors
    • Kalyan Veeramachaneni, Massachusetts Institute of Technology, Cambridge, MA, USA

Tracer spectrum: a visualisation method for distributed evolutionary computation

Abstract  We present a novel visualisation method for island-based evolutionary algorithms based on the concept of tracers as adopted in medicine and molecular biology to follow a biochemical process. For example, a radioisotope or dye can be…

Abstract  

We present a novel visualisation method for island-based evolutionary algorithms based on the concept of tracers as adopted in medicine and molecular biology to follow a biochemical process. For example, a radioisotope or dye can be used
to replace a stable component of a biological compound, and the signal from the radioisotope can be monitored as it passes
through the body to measure the compound’s distribution and elimination from the system. In a similar fashion we attach a
tracer dye to individuals in each island, where each individual in any one island is marked with the same colour, and each island then has its own unique colour signal. We can then monitor how individuals undergoing migration events are
distributed throughout the entire island ecosystem, thereby allowing the user to visually monitor takeover times and the resulting
loss of diversity. This is achieved by visualising each island as a spectrum of the tracer dye associated with each individual.
Experiments adopting different rates of migration and network connectivity confirm earlier research which predicts that island
models are extremely sensitive to the size and frequency of migrations.

  • Content Type Journal Article
  • Pages 161-171
  • DOI 10.1007/s10710-010-9125-y
  • Authors
    • Michael O’Neill, Natural Computing Research and Applications Group, Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland
    • Anthony Brabazon, Natural Computing Research and Applications Group, Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland
    • Erik Hemberg, Natural Computing Research and Applications Group, Complex and Adaptive Systems Laboratory, University College Dublin, Dublin, Ireland

Have your spaghetti and eat it too: evolutionary algorithmics and post-evolutionary analysis

Abstract  This paper focuses on two issues, first perusing the idea of algorithmic design through genetic programming (GP), and, second,
introducing a novel approach for analyzing and understanding the evolved solution trees. Considering the…

Abstract  

This paper focuses on two issues, first perusing the idea of algorithmic design through genetic programming (GP), and, second,
introducing a novel approach for analyzing and understanding the evolved solution trees. Considering the problem of list search, we evolve iterative algorithms for searching for a given key in an array of integers, showing that both correct linear-time
and far more efficient logarithmic-time algorithms can be repeatedly designed by Darwinian means. Next, we turn to the (evolved)
dish of spaghetti (code) served by GP. Faced with the all-too-familiar conundrum of understanding convoluted—and usually bloated—GP-evolved
trees, we present a novel analysis approach, based on ideas borrowed from the field of bioinformatics. Our system, dubbed
G-PEA (GP Post-Evolutionary Analysis), consists of two parts: (1) Defining a functionality-based similarity score between expressions,
G-PEA uses this score to find subtrees that carry out similar semantic tasks; (2) Clustering similar sub-expressions from a number of independently evolved fit solutions, thus identifying important
semantic building blocks ensconced within the hard-to-read GP trees. These blocks help identify the important parts of the evolved solutions and are
a crucial step in understanding how they work. Other related GP aspects, such as code simplification, bloat control, and building-block
preserving crossover, may be extended by applying the concepts we present.

  • Content Type Journal Article
  • Pages 121-160
  • DOI 10.1007/s10710-010-9122-1
  • Authors
    • Kfir Wolfson, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel
    • Shay Zakov, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel
    • Moshe Sipper, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel
    • Michal Ziv-Ukelson, Department of Computer Science, Ben-Gurion University, 84105 Beer-Sheva, Israel

Eureqa: software review

Eureqa: software review
Content Type Journal ArticlePages 173-178DOI 10.1007/s10710-010-9124-zAuthors
Renáta Dubčáková, Faculty of Safety Engineering, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

Journal Genetic Programmi…

Eureqa: software review

  • Content Type Journal Article
  • Pages 173-178
  • DOI 10.1007/s10710-010-9124-z
  • Authors
    • Renáta Dubčáková, Faculty of Safety Engineering, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

Arthur K. Kordon: Applying computational intelligence: how to create value

Arthur K. Kordon: Applying computational intelligence: how to create value
Content Type Journal ArticlePages 85-86DOI 10.1007/s10710-010-9120-3Authors
Guillermo Leguizamón, Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, U…

Arthur K. Kordon: Applying computational intelligence: how to create value

  • Content Type Journal Article
  • Pages 85-86
  • DOI 10.1007/s10710-010-9120-3
  • Authors
    • Guillermo Leguizamón, Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Universidad Nacional de San Luis, San Luis, Argentina

Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies

Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies
Content Type Journal ArticlePages 89-89DOI 10.1007/s10710-010-9123-0Authors
Ivan Garibay, University of Central Florida, Orla…

Erratum to: Dario Floreano and Claudio Mattiussi: Bio-inspired artificial intelligence: theories, methods, and technologies

  • Content Type Journal Article
  • Pages 89-89
  • DOI 10.1007/s10710-010-9123-0
  • Authors
    • Ivan Garibay, University of Central Florida, Orlando, FL USA

Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach

Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach
Content Type Journal ArticlePages 81-83DOI 10.1007/s10710-010-9119-9Authors
John Woodward, The University of Nottingham, Ningbo, Chi…

Gisele L. Pappa, Alex Freitas: Automating the design of data mining algorithms, an evolutionary computation approach

  • Content Type Journal Article
  • Pages 81-83
  • DOI 10.1007/s10710-010-9119-9
  • Authors
    • John Woodward, The University of Nottingham, Ningbo, China