Skip to main content

19 Result(s)

within Dr. J. E. Smith Remove this filter

and

  1. No Access

    Book

    Recent Advances in Memetic Algorithms

    William E. Hart, Dr. J. E. Smith… in Studies in Fuzziness and Soft Computing (2005)

  2. No Access

    Chapter

    Gray Coding

    Gray coding is a variation on the way that integers are mapped on bit strings that ensures that consecutive integers always have Hamming distance one. A three bit Gray coding table is given in Table A.l, and t...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  3. No Access

    Book

    Introduction to Evolutionary Computing

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Natural Computing Series (2003)

  4. No Access

    Chapter

    Evolution Strategies

    In this chapter we introduce evolution strategies (ES), another member of the evolutionary algorithm family. We also use these algorithms to illustrate a very useful feature in evolutionary computing: self-adapta...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  5. No Access

    Chapter

    Genetic Programming

    In this chapter we present genetic programming, the youngest member of the evolutionary algorithm family. Besides the particular representation (using trees as chromosomes), it differs from other EA strands in...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  6. No Access

    Chapter

    Summary

    In this book we have presented evolutionary computing as one problem-solving paradigm and positioned four historical types of EAs as “dialects”. These dialects have emerged independently to some extent (except GP...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  7. No Access

    Chapter

    Test Functions

    We cannot hope here to give a comprehensive set of test functions, and by the arguments given in Sect. 14.4.1, it would not be particularly appropriate. Rather we will give a few instances of test problems that w...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  8. No Access

    Chapter

    Evolutionary Programming

    In this chapter we present evolutionary programming (EP), another historical member of the EC family. Other EC streams have an algorithm variant that can be identified as being the “standard”, or typical, vers...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  9. No Access

    Chapter

    Learning Classifier Systems

    This chapter introduces an evolutionary approach to machine learning tasks working with rule sets, rather than parse trees, to represent knowledge. In learning classifier systems (LCS) the evolutionary algorit...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  10. No Access

    Chapter

    What is an Evolutionary Algorithm?

    The most important aim of this chapter is to describe what an evolutionary algorithm is. This description is deliberately based on a unifying view presenting a general scheme that forms the common basis of all...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  11. No Access

    Chapter

    Parameter Control in Evolutionary Algorithms

    The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this chapter we discuss how to do this, beginning with the issue of whether these values a...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  12. No Access

    Chapter

    Theory

    In this chapter we present a brief overview of some of the approaches taken to analysing and modelling the behaviour of Evolutionary Algorithms. The “Holy Grail” of these efforts is the formulation of predicti...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  13. No Access

    Chapter

    Special Forms of Evolution

    In this chapter we discuss special forms of evolution that in some sense deviate from the standard evolutionary algorithms. In particular, we present coevolution and interactive evolution that both work under ...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  14. No Access

    Chapter

    Introduction

    Evolutionary computing is a research area within computer science. As the name suggests, it is a special flavour of computing, which draws inspiration from the process of natural evolution- That some computer ...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  15. No Access

    Chapter

    Genetic Algorithms

    In this chapter we describe the most widely known type of evolutionary algorithm: the genetic algorithm. After presenting a simple example to introduce the basic concepts, we begin with what is usually the mos...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  16. No Access

    Chapter

    Multimodal Problems and Spatial Distribution

    So far in our discussion of evolutionary algorithms we have considered the entire population to act as a common genepool, with fitness as the primary feature affecting the likelihood of an individual taking pa...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  17. No Access

    Chapter

    Hybridisation with Other Techniques: Memetic Algorithms

    In the preceding chapters we described the main varieties of evolutionary algorithms and described various examples of how they might be suitably implemented for different applications. In this chapter we turn...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  18. No Access

    Chapter

    Constraint Handling

    In this chapter we consider the issue of constraint handling by evolutionary algorithms. This issue has great practical relevance because many practical problems are constrained. It is also a theoretically cha...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)

  19. No Access

    Chapter

    Working with Evolutionary Algorithms

    The main objective of this chapter is to provide practical guidelines for working with EAs. Working with EAs often means comparing different versions experimentally. Guidelines to perform experimental comparis...

    Prof. Dr. A. E. Eiben, Dr. J. E. Smith in Introduction to Evolutionary Computing (2003)