Chapter 2Breaking Down Genetic Algorithms
In the previous chapter, you learned about informed search and why it’s superior to brute-force search. You were introduced to genetic algorithms and saw how they balance exploitation and exploration for different problems. You used this knowledge to tackle the One-Max problem, which is an introductory optimization problem.
While your previous solution to the One-Max problem was effective, it’s difficult to both tweak and expand. More advanced applications of genetic algorithms will require extensive fine-tuning and experimentation to achieve the best results, which means you need to create modular and easily customizable solutions.
In this chapter, you’ll once again attack the One-Max problem; but ...
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