conversations.from: human
conversations.value: Give me a description for a book titled Genetic Algorithms with Python about Python, Step-by-step with the genre: Computers & Technology, Programming.
conversations.from: assistant
conversations.value: Get a hands-on introduction to machine learning with genetic algorithms using Python. Step-by-step tutorials build your skills from Hello World! to optimizing one genetic algorithm with another, and finally genetic programming; thus preparing you to apply genetic algorithms to problems in your own field of expertise.
conversations.from: human
conversations.value: Give me the first five paragraphs for this book.
conversations.from: assistant
conversations.value: Genetic Algorithms with Python
  It finds the optimal solution every time but overcoming the local minimum at 74.00 takes the majority of the time. In benchmarks it takes 8.69 seconds +/- 8.02 seconds, which is much too slow for us to want to use in benchmarks.
  When you read TSP research papers online the conversation invariably focuses on a genetic algorithm strategy that we haven't discussed yet - crossover. Crossover uses features from two different parents to create the new child. The idea is that the genes of each parent have part of the solution but they both may be stuck at a local minimum or maximum. So, if random parts of both are combined, it might produce a better solution to the problem.
  The reason conversation focuses on crossover is because of the problem constraints and the many ways people have tried to work around them. For example, you can't just copy random genes because you might introduce duplication, which results in a worse fitness and a wasted attempt. The solution to the TSP is a cycle through all points, but the parent's genes for producing that route could start anywhere in the cycle and travel either direction along it. This means even if you use a clever method to copy genes to the new child so that you don't introduce duplication, you may be replacing clockwise-cycling genes with counter-clockwise cycling ones, which may or may not be an improvement. You may also, depending on how you select the genes that get moved, be breaking up a run that is already optimal. Ultimately, the point is that you will have to engineer crossover at least as much if not more than you do mutation.
  First things first. A prerequisite for crossover is two parents. Right now we only have one parent, so we're going to add the ability to set the number of parents.
  Then populate the `parents` array by generating new random parents. If one that has a better fitness than the best parent is found, we replace the best parent and update the list of historical best fitnesses.
conversations.from: human
conversations.value: Give me five more paragraphs.
conversations.from: assistant
conversations.value: A variable will contain the instructions to be repeated. When the `execute` function is called it will execute that list of instructions the number of times requested.
  Lastly, we need to provide a textual representation for `display`. If `Ops` is populated we'll combine the instructions' string representations into a string and return it, otherwise we'll use `StepCount`.
  Wow! We can mow the field with only 6 instructions! However, there are a couple of issues to be resolved. The `display` output is now messy because the step count has gone to 3 digits. It has never been that high before. Also, occasionally the engine builds a very deep set of repeats that causes a `RecursionError` to be thrown. When that happens we can just catch the error and move on trusting the genetic engine to eliminate that gene sequence.
  Another issue is fuel efficiency. If the `mower` used fuel for each step, the set of instructions above would cost us almost 4 times as much as the hard coded but optimal solution. To fix this we're going to change `Fitness` to prefer fewer steps to fewer instructions.
  Now when we run the test it takes longer but produces a more fuel efficient yet still quite short set of instructions.
conversations.from: human
conversations.value: Delete the last sentence.
conversations.from: assistant
conversations.value: A variable will contain the instructions to be repeated. When the `execute` function is called it will execute that list of instructions the number of times requested.
  Lastly, we need to provide a textual representation for `display`. If `Ops` is populated we'll combine the instructions' string representations into a string and return it, otherwise we'll use `StepCount`.
  Wow! We can mow the field with only 6 instructions! However, there are a couple of issues to be resolved. The `display` output is now messy because the step count has gone to 3 digits. It has never been that high before. Also, occasionally the engine builds a very deep set of repeats that causes a `RecursionError` to be thrown. When that happens we can just catch the error and move on trusting the genetic engine to eliminate that gene sequence.
  Another issue is fuel efficiency. If the `mower` used fuel for each step, the set of instructions above would cost us almost 4 times as much as the hard coded but optimal solution. To fix this we're going to change `Fitness` to prefer fewer steps to fewer instructions.
conversations.from: human
conversations.value: Give me the last five paragraphs.. Start with the sentence 'Next each gene sequence plays against every other gene sequence both as first-to-move and as the opponent, keeping track of wins, losses and ties'.
conversations.from: assistant
conversations.value: Next each gene sequence plays against every other gene sequence both as first-to-move and as the opponent, keeping track of wins, losses and ties.
  Finally, a new generation is created by crossbreeding the best N gene sequences. They are also included in the new generation along with 1 additional randomly generated gene sequence. When the maximum number of generations have completed, the function returns the best gene sequence that was seen.
  This chapter introduced a new method of sampling and recombining gene sequences to find a good combination. One potential usage is to quickly find a starting place for a slower, more comprehensive survey of the problem space.
  Get a hands-on introduction to building and using decision trees and random forests. Tree-based machine learning algorithms are used to categorize data based by known outcomes in order to facilitate predicting outcomes in new situations.
  You will learn not only how to use decision trees and random forests for classification and regression, and their respective limitations, but also how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you.