Population pool holds all the chromosomes usually 20

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Population pool holds all the chromosomes (usually 20 individuals or above) Every chromosome is a potential solution of a problem The Quality of a chromosome is governed by a Fitness evaluation process (Objective Function) Head color size Body color feet body wings head Antennae Fitness Functions 1. Head: 1. All corrected = 8 2. 1 bit corrected= 4 3. All wrong = 0 2. Body: 1. All corrected = 8 2. 2 bits corrected = 4 3. 1 bit corrected = 2 4. All wrong = 0 3. Wings: 1. All corrected = 6 2. Otherwise = 0 4. Size: 1. Corrected size = 8 2. One size level difference = 4 3. Two size levels difference = 2 4. Three size levels difference = 0
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Selection - Proportionate scheme Roulette Wheel Selection 10 18 22 Roulette Wheel I II III 1 10 11 32 33 50 Roulette Wheel Selection 1. Sum the fitness of all the population members; named as total fitness ( F = 22+10+18 ) 2. Generate a random number (n) between 1 and total fitness F (for example: n=25 ) 3. Return the first population member whose fitness added to the fitness of the preceding population members, is greater than or equal to n . (for example: 10+22>25 ) Rationale fitter individual higher chance However, fittest may not be the best
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Crossover A major operation in GA A technique to combine the genes of two parents A crossover rate to govern the probability of the operation (usually very high, 0.6- 1.0) Offspring may be the same as their parents Single Point Crossover
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Mutation A background operation with low operational rate (<0.1) A bitwise operation applied to each offspring after crossover Provide the missing gene Increase the randomness Mutation
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Selection window of functions and parameters Generation
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A sample result of the program Best fitness value plot
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Average fitness value plot General Rules of Simple GA Homogenous chromosome structure Finite population size, and it is maintained in evolution process High crossover rate & Low mutation rate Replacement: Generational or steady state
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Analogue DNA
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