ai-ga - Introduction to Genetic Algorithms 1 Genetic...

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1 Introduction to Genetic Algorithms
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2 Genetic Algorithms What are they? Evolutionary algorithms that make use of operations like mutation, recombination, and selection Uses? Difficult search problems Optimization problems Machine learning Adaptive rule-bases
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3 Theory of Evolution Every organism has unique attributes that can be transmitted to its offspring Offspring are unique and have attributes from each parent Selective breeding can be used to manage changes from one generation to the next Nature applies certain pressures that cause individuals to evolve over time
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4 Evolutionary Pressures Environment Creatures must work to survive by finding resources like food and water Competition Creatures within the same species compete with each other on similar tasks Rivalry Different species affect each other by direct confrontation (e.g. hunting) or indirectly by fighting for the same resources
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5 Natural Selection Creatures that are not good at completing tasks like hunting have fewer chances of having offspring Creatures that are successful in completing basic tasks are more likely to transmit their attributes to the next generation since there will be more creatures born that can survive and pass on these attributes
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6 Genetics Genome (class) Sequence of genes describing the overall structure of the genetic for a particular species Genomics Study of the meaning of the genes for a particular species Alleles Values that can be assigned to a given gene Genotype (instance) Sequence of alleles
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7 Physical Properties Phenetics Study of physical properties and morphology of creatures independent of genetic information Phenome General structure of creatures body and attributes Phenotype Particular instance of phenome realized as a unique creature Product of genotype and environment forces
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8 Conversions In real-world mapping between genotypes and phenotypes is hard In AI work it can be done by defining a convenient function or even designing encodings by hand It is often easier to adapt genetic operators to work with the evolutionary data structure used to represent the phenotype than to encode and decode phenotypes
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9 Genetic Algorithmic Process Potential solution for problem domains are encoded using machine representation (e.g. bit strings) that supports variation and selection operations Mating and mutation operations produce new generation of solutions from parent encodings Fitness function judges the individuals that are “best” suited (e.g. most appropriate problem solution) for “survival”
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10 Initialization Initial population must be a representative sample of the search space Random initialization can be a good idea (if the sample is large enough) Random number generator can not be biased Can reuse or seed population with existing genotypes based on algorithms or expert opinion or previous evolutionary cycles
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ai-ga - Introduction to Genetic Algorithms 1 Genetic...

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