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Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
Genetic Algorithm
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Genetic Algorithm

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This is an explanation of what is genetic algorithm and the use of it.

This is an explanation of what is genetic algorithm and the use of it.

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  • Two parentsAttract matesOther hunting foodChild both characteristicsSuperfit
  • There should not be too many local maximasThere shouldn’t be a single global maximumTherefore better chromosomes must be closely related.Problem: Preparing a time table.Invalid chromosomes must guide towards valid chromosomes, using fitness valueBetter to invest sub-goals rewarded going for the ultimate goalPremature convergence: Set of highly fit individuals come to dominate the population, making a local maxima. Which in turn make it difficult to converge to more effective solutionSlow finishing: Population might have converged, but might not have found a global maxima
  • Passing the genes without disruption of crossoverProbability 0.001
  • Better individuals -> more of their genes to next generationGood schemata - Likelihood of better solution increase
  • Power of GA – Finding good building blocksInteraction – In a chromosome, contribution of a gene to the fitness value depends on values of other genesIssues: Might not allow closely related genes to be placed togetherInteraction between genes can become high (specially in multi-dimensional data)
  • AssumptionsPopulation is infiniteFitness function accurately shows the effectiveness of a solutionGenes in a chromosome don’t interact significantlyGenetic drift, mutation rate
  • Transcript

    • 1. An overview of Genetic AlgorithmBy David Beasley, David R. Bull and Ralph R. Martin 090070T – T.P.K. Dahanayakage 090150N – K.M.T.V. Ganegedara
    • 2. Introduction Population Evolves  Natural Selection  Survival of the fittest Applications?  Computer, Bridges, Garment, etc.
    • 3. Analogy Think about successive generations
    • 4. Analogy (ctd) Super-fit Evolve according to environment
    • 5. Basic Concept Set of solutions for a problem  Each solution – fitness score Reproduce a new set of solutions by “Cross-breeding”  Most-fit: get selected  Least-fit: not selected – die out Result?  Offsprings with characteristics from most- fit
    • 6. What just happened? Good characteristics of a generation was spread in a successive generation Most promising areas of solution space are searched
    • 7. AlgorithmBEGIN Generate population Calculate fitness for each individual WHILE NOT CONVERGED DO BEGIN FOR population_size/2 DO BEGIN Select 2 parents for mating Combine and produce an offspring Calculate the fitness for the new individual Insert the offspring to the new generation END ENDEND
    • 8. Lesson on Biology Chromosome  Organized collection of coiled DNA DNA
    • 9. Fitness function Must represent the “fitness to the environment” or “ability” of a chromosome’s Issues of fitness range  Premature convergence  Slow finishing
    • 10. Reproduction Selection of parents  Random  Favors the fittest Crossover  Single point crossover  Cut 2 chromosomes at a random point  Swap over tails to create 2 new chromosomes
    • 11. Reproduction (ctd) Crossover is not the only case!  0.6 - 1.0 chance  Otherwise replicate the parent Mutation  Alter the genes of crossover-ed with a small probability
    • 12. Example 0101001100 1011001001 0101001001 1011001100 Before mutation: 0 1 0 1 0 0 1 0 0 1 After mutation: 0101101001
    • 13. Convergence Fitness of the BEST and AVERAGE moves to a global optimum Gene is said to have converged  95% of the population has converged Population is said to have converged  All the genes have converged
    • 14. Other techniques
    • 15. “Schemata” and “Scheme” Definition of Schema  Pattern of gene  String comprise {0,1,#} Ex: Chromosome 0110 contains following “Schemata”  #110, #1#0, 01##, etc. A chromosome is said to contain a schema if it matches a particular schemata
    • 16.  Order of schema – Number of non-# symbols Length of schema – Distance between outer most non-# symbols. Ex: #1#0
    • 17. Schema Theorem Individuals in a population are given reproductive trials Number of trials α Fitness of an individual Higher fitness value -> Good schemata Good Schemata receives exponentially increasing number of trials in successive generations!
    • 18. Building Block Hypothesis Definition  Schemata short in length and tend to improve performance when incorporated to an individual Properties of a successful coding scheme  Related genes close together  Little interaction between genes
    • 19. Exploration and Exploitation Exploration  Exploring unknown areas Exploitation  Utilizing already-learnt to find better solutions Tradeoff  Ex: Random search and Hill climbing GA combines both in an optimal way!
    • 20. Practical Aspects of GA
    • 21. Parent selection Individuals are copied to a “mating pool”  Highly fit – more copies  Less fit – lesser copies How to determine number of copies?  Explicit fitness remapping  Implicit fitness remapping
    • 22. Explicit fitness remapping Individual’s fitness Average fitness of population Issue: Number of copies should be an integer Solution:  Fitness scaling  Fitness windowing
    • 23. Implicit fitness remapping Tournament selection  2 random individuals  Copy the one with higher fitness value to the mating pool  Continue until the pool is full
    • 24. Generation gaps andsteady-state replacement Generation gap  Proportion of individuals in a population replaced in each generation Steady-state replacement  Only few individuals are replaced in a generation  Considerations:  Parent selection – Random, Fitness  Replacement – Random, Inverse fitness
    • 25. Thank you Q & A Session

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