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Binary-Coded Genetic Algorithm Lecture 22 ME EN 575 Andrew Ning - - PDF document
Binary-Coded Genetic Algorithm Lecture 22 ME EN 575 Andrew Ning - - PDF document
Binary-Coded Genetic Algorithm Lecture 22 ME EN 575 Andrew Ning aning@byu.edu Outline Overview Binary-Coded GA Overview Genetic algorithms (GAs) are based on three main concepts: Algorithm Important differences from our past algorithms:
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Algorithm
Important differences from our past algorithms:
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Binary-Coded GA
Consider the following simple example minimizing the cost of a can∗. minimize πd2 2 + πdh subject to πd2h 4 ≥ 300 ml dmin ≤ d ≤ dmax hmin ≤ h ≤ hmax
∗ Multi-objective Optimization Using Evolutionary Algorithms, Kalyanmoy
Deb
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Convert the following numbers to binary: d = 8, h = 10 Combine into one “chromosome”: d = 01000, h = 01010 x = 0100001010
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Initialize Population and Evaluate Fitness
Create a random initial population.
∗ A good way to do this is with Latin Hypercube Sampling (will take about
this later in the semester in connection with Surrogate-Based Optimization).
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Selection: Survival of the Fittest
Tournament
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New population:
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Roulette Wheel:
Reproduction
Single-point crossover: Parents: 1 1 1 1 1 1 1 1 Offspring: 1 1 1 1 1 1 1 1
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