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Genetic Algorithms

Genetic Algorithms. Ranga Rodrigo March 5, 2014. Evolutionary Computation (EC). Introduction to Evolutionary Computation. Evolution is this process of adaption with the aim of improving the survival capabilities through processes such as natural selection, survival of the fittest,

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Genetic Algorithms

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  1. Genetic Algorithms Ranga Rodrigo March 5, 2014

  2. Evolutionary Computation (EC)

  3. Introduction to Evolutionary Computation • Evolution is this process of adaption with the aim of improving the survival capabilities through processes such as • natural selection, • survival of the fittest, • reproduction, • mutation, • competition and • symbiosis.

  4. DNA, the molecular basis for inheritance. Each strand of DNA is a chain of nucleotides, matching each other in the center to form what look like rungs on a twisted ladder. http://en.wikipedia.org/wiki/Genetics

  5. A Punnett square depicting a cross between two pea plants heterozygous for purple (B) and white (b) blossoms. At its most fundamental level, inheritance in organisms occurs by passing discrete heritable units, called genes, from parents to progeny.[31] This property was first observed by Gregor Mendel, who studied the segregation of heritable traits in pea plants.[12][32] In his experiments studying the trait for flower color, Mendel observed that the flowers of each pea plant were either purple or white—but never an intermediate between the two colors. These different, discrete versions of the same gene are called alleles. http://en.wikipedia.org/wiki/Genetics

  6. Evolutionary Computing (EC) • Evolutionary computing models the processes of natural evolution. • It is a computer-based problem solving systems that use computational models of evolutionary processes, such as natural selection, survival of the fittest and reproduction.

  7. Evolutionary Algorithm Paradigms

  8. Genetic Algorithms (GA)

  9. Introduction to GA • Genetic algorithms imitate natural optimization process, natural selection in evolution. • Developed by John Holland at the University of Michigan for machine learning in 1975. • Mostly for binary representations.

  10. Evolutionary Search Process

  11. Start • Initiation: Selection of initial population of chromosomes • Evaluation of the fitness of chromosomes in the population Stopping criterion No Yes Presentation of the “best” chromosome • Selection of chromosomes • Application of genetic operators Stop • Creating a new population

  12. Selection (Roulette Wheel) • The fittest individuals must have the greatest chance of survival. • Probability of being selected http://www.edc.ncl.ac.uk/highlight/rhjanuary2007g02.php/

  13. Genetic Operators • Crossover: combination of genetic material randomly selected from two or more parents. • Mutation: process of randomly changing the values of genes in a chromosome.

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