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Background on Genetic Algorithms

  • Walker H. LandJr.
  • J. David Schaffer
Chapter
  • 346 Downloads

Abstract

This chapter introduces evolutionary computation/genetic algorithms starting at a high level. It uses the schema sampling theorem to provide an intuitive understanding for how evolution, operating on a population of chromosomes (symbol strings), will produce offspring that contain variants of the symbol patterns in the more fit parents each generation, and shows how the recombination operators will be biased for and against some patterns. The No Free Lunch (NFL) theorem of Wolpert and Macready for optimization search algorithms has shown that over the space of all possible problems, there can be no universally superior algorithm. Hence, it is incumbent on any algorithm to attempt to identify the domain of problems for which it is effective and try to identify its strengths and limitations. In the next section, we introduce Eshelman’s CHC genetic algorithm and recombination operators that have been developed for bit string and integer chromosomes. After showing its strengths particularly in dealing with some of the challenges for traditional genetic algorithms, its limitations are also shown. The final section takes up the application of CHC to subset selection problems, a domain of considerable utility for many machine learning applications. We present a series of empirical tests that lead us to the index chromosome representation and the match and mix set-subset size (MMX_SSS) recombination operator that seem well suited for this domain. Variants are shown for when the size of the desired subset is known and when it is not known. We apply this algorithm in later chapters to the feature subset selection problem that is key to our application of developing a speech-based diagnostic test for dementia.

Keywords

Evolutionary computation Genetic algorithms CHC algorithm Schema sampling theorem Subset selection Hierarchical multiple objective selection Incest prevention Soft restart Interval schemata Blend crossover 

Abbreviations

2X

Two-point crossover

BLX

Blend crossover

CHC

A GA with a specific set of operators introduced by Eshelman (1991)

EC

Evolutionary computation

EE

Exhaustive enumeration an algorithm that simply tests all possible alternatives

EP

Evolutionary programming

FR

Fitness ratio the ratio of the average fitness for observed members of a schema to the current population average

GA

Genetic algorithm

GP

Genetic programming a GA variant using function trees to evolve computer programs

HC

Hill climber

HUX

Half uniform crossover

IEEE

Institute of Electrical and Electronics Engineers

IHC

Iterated hill climber

LKBX

A set recombination operator introduced by Lucasius and Kateman (1992)

MMX

Match and mix set recombination operator

MMX_SSS

MMX for unknown subset sizes

MRAR

Modified RAR

NFL

No Free Lunch theorem of Wolpert and McCready

RAR

Random assorting recombination

RBC

Random bit climber a kind of HC

RBC+

RBC augmented with soft restart capability

RRAR

Radcliff’s RAR

RRR

Random respectful recombination

RS

Random search

SA

Simulated annealing a stochastic search algorithm

SGA

A simple GA

SRAR

Simple RAR

sss

Subset size

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Walker H. LandJr.
    • 1
  • J. David Schaffer
    • 2
  1. 1.Binghamton UniversityBowieUSA
  2. 2.Binghamton UniversityBinghamtonUSA

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