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Machine Evolution
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Evolutions Generations of descendants Search processes
Production of descendants changed from their parents Selective survival Search processes Searching for high peaks in the hyperspace
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Applications Function optimization Solving specific problems
The maximum of a function John Holland Solving specific problems Control reactive agents Classifier systems Genetic programming
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A program expressed as a tree
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A robot to follow the wall around forever
Primitive functions : AND, OR, NOT, IF Boolean functions AND(x,y) = 0 if x = 0; else y OR(x,y) = 1 if x = 1; else y NOT(x) = 0 if x = 1; else 1 IF(x,y,Z) = y if x = 1; else z Actions North, east, south, west
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A robot to follow the wall around forever
All of the action functions have their indicated effects unless the robot attempts to move into the wall Sensory inputs ::: n, ne, e, se, s , sw, w, nw 만약 함수의 수행결과가 값이 없으면 중지
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A robot in a Grid World
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A wall following program
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The GP process Generation 0 (0세대): start with a population of random programs with functions, constants, and sensory inputs 5000 random programs Final : Generation 62 60 steps 동안 벽에 있는 방을 방문한 횟수로 평가 32 cells이면 perfects; 10곳에서 출발하여 fitness 측정
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Generation of populations I
(i+1)th generation 10%는 i-the generation에서 copy 5000 populations에서 무작위로 7개를 선택하여 가장 우수한 것을 선택 (tournament selection) 90%는 앞의 방법으로 두 프로그램(a mother, a father)을 선택하여, 무작위로 선정한 father의 subtree를 mother의 subtree에 넣는다 (crossover)
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Crossover
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Generation of populations II
Mutation : 1%를 tournament로 선정 무작위로 선택한 subtree를 제거하고, 1세대에서 개체를 생성하는 방법으로 만들어서 끼워넣는다
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Evolving a wall-following robot
개별 프로그램의 예 (AND (sw) (ne)) (with fitness 0) (OR (e) (west) (with fitness 5(?)) the best one ::: fitness = 92 (어떤 때)
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The most fit individual in generation 0
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The most fit individuals in generation 2
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The most fit individuals in generation 6
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The most fit individuals in generation 10
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Fitness as a function of generation number
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