January 2020
Intermediate to advanced
346 pages
9h 8m
English
Content preview from Hands-On Genetic Algorithms with Python
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Fitness function
At each iteration of the algorithm, the individuals are evaluated using a fitness function (also called the target function). This is the function we seek to optimize or the problem we attempt to solve.
Individuals who achieve a better fitness score represent better solutions and are more likely to be chosen to reproduce and be represented in the next generation. Over time, the quality of the solutions improves, the fitness values increase, and the process can stop once a solution is found with a satisfactory fitness value.
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