.

0 (hodnocen0 x )

EB

EB

ONLINE

2nd ed.

Birmingham : Packt Publishing, Limited, 2024

1 online zdroj (419 stran)

ISBN 9781805121572 (e-kniha)

ISBN 9781805123798

Print version: Wirsansky, Eyal Hands-On Genetic Algorithms with Python Birmingham : Packt Publishing, Limited,c2024 ISBN 9781805123798

002001779

Preface -- Part 1: The Basics of Genetic Algorithms -- Chapter 1: An Introduction to Genetic Algorithms -- What are genetic algorithms? -- Darwinian evolution -- The genetic algorithms analogy -- The theory behind genetic algorithms -- The schema theorem -- Differences from traditional algorithms -- Population-based -- Genetic representation -- Fitness function -- Probabilistic behavior -- Advantages of genetic algorithms -- Global optimization -- Handling complex problems -- Handling a lack of mathematical representation -- Resilience to noise -- Parallelism -- Continuous learning -- Limitations of genetic algorithms -- Special definitions -- Hyperparameter tuning -- Computationally intensive -- Premature convergence -- No guaranteed solution -- Use cases for genetic algorithms -- Summary -- Further reading -- Chapter 2: Understanding the Key Components of Genetic Algorithms -- The basic flow of a genetic algorithm -- Creating the initial population -- Calculating the fitness -- Applying selection, crossover, and mutation -- Checking the stopping conditions -- Selection methods -- Roulette wheel selection -- Stochastic universal sampling -- Rank-based selection -- Fitness scaling -- Tournament selection -- Crossover methods -- Single-point crossover -- Two-point and k-point crossover -- Uniform crossover -- Crossover for ordered lists -- Mutation methods -- Flip-bit mutation -- Swap mutation -- Inversion mutation -- Scramble mutation -- Real-coded genetic algorithms -- Blend crossover -- Simulated binary crossover -- Real mutation -- Understanding elitism -- Niching and sharing -- Serial niching versus parallel niching -- The art of solving problems using genetic algorithms -- Summary -- Further reading -- Part 2: Solving Problems with Genetic Algorithms.

Chapter 3: Using the DEAP Framework -- Technical requirements -- Python version -- Using a virtual environment -- Installing the necessary libraries -- Introduction to DEAP -- Using the creator module -- Creating the Fitness class -- Creating the Individual class -- Using the Toolbox class -- Creating genetic operators -- Creating the population -- Calculating the fitness -- The OneMax problem -- Solving the OneMax problem with DEAP -- Choosing the chromosome -- Calculating the fitness -- Choosing the genetic operators -- Setting the stopping condition -- Implementing with DEAP -- Using built-in algorithms -- The Statistics object -- The algorithm -- The logbook -- Running the program -- Adding the hall of fame feature -- Experimenting with the algorithm’s settings -- Population size and number of generations -- Crossover operator -- Mutation operator -- Selection operator -- Summary -- Further reading -- Chapter 4: Combinatorial Optimization -- Technical requirements -- Search problems and combinatorial optimization -- Solving the knapsack problem -- The Rosetta Code knapsack 0-1 problem -- Solution representation -- Python problem representation -- Genetic algorithm solution -- Solving the TSP -- TSPLIB benchmark files -- Solution representation -- Python problem representation -- Genetic algorithm solution -- Improving the results with enhanced exploration and elitism -- Solving the VRP -- Solution representation -- Python problem representation -- Genetic algorithm solution -- Summary -- Further reading -- Chapter 5: Constraint Satisfaction -- Technical requirements -- Constraint satisfaction in search problems -- Solving the N-Queens problem -- Solution representation -- Python problem representation -- Genetic algorithms solution -- Solving the nurse scheduling problem -- Solution representation -- Hard constraints versus soft constraints.

Python problem representation -- Genetic algorithms solution -- Solving the graph coloring problem -- Solution representation -- Using hard and soft constraints for the graph coloring problem -- Python problem representation -- Genetic algorithms solution -- Summary -- Further reading -- Chapter 6: Optimizing Continuous Functions -- Technical requirements -- Chromosomes and genetic operators for real numbers -- Using DEAP with continuous functions -- Optimizing the Eggholder function -- Optimizing the Eggholder function with genetic algorithms -- Improving the speed with an increased mutation rate -- Optimizing Himmelblau’s function -- Optimizing Himmelblau’s function with genetic algorithms -- Using niching and sharing to find multiple solutions -- Simionescu’s function and constrained optimization -- Constrained optimization with genetic algorithms -- Optimizing Simionescu’s function using genetic algorithms -- Using constraints to find multiple solutions -- Summary -- Further reading -- Part 3: Artificial Intelligence Applications of Genetic Algorithms -- Chapter 7: Enhancing Machine Learning Models Using Feature Selection -- Technical requirements -- Supervised machine learning -- Classification -- Regression -- Supervised learning algorithms -- Feature selection in supervised learning -- Selecting the features for the Friedman-1 regression problem -- Solution representation -- Python problem representation -- Genetic algorithms solution -- Selecting the features for classifying the Zoo dataset -- Python problem representation -- Genetic algorithms solution -- Summary -- Further reading -- Chapter 8: Hyperparameter Tuning of Machine Learning Models -- Technical requirements -- Hyperparameters in machine learning -- Hyperparameter tuning -- The Wine dataset -- The adaptive boosting classifier.

Tuning the hyperparameters using conventional versus genetic grid search -- Testing the classifier’s default performance -- Running the conventional grid search -- Running the genetic-algorithm-driven grid search -- Tuning the hyperparameters using a direct genetic approach -- Hyperparameter representation -- Evaluating the classifier accuracy -- Tuning the hyperparameters using genetic algorithms -- Dedicated libraries -- Summary -- Further reading -- Chapter 9: Architecture Optimization of Deep Learning Networks -- Technical requirements -- ANNs and DL -- MLP -- DL and convolutional NNs -- Optimizing the architecture of a DL classifier -- The Iris flower dataset -- Representing the hidden layer configuration -- Evaluating the classifier’s accuracy -- Optimizing the MLP architecture using genetic algorithms -- Combining architecture optimization with hyperparameter tuning -- Solution representation -- Evaluating the classifier’s accuracy -- Optimizing the MLP’s combined configuration using genetic algorithms -- Summary -- Further reading -- Chapter 10: Reinforcement Learning with Genetic Algorithms -- Technical requirements -- Reinforcement learning -- Genetic algorithms and reinforcement learning -- Gymnasium -- The env interface -- Solving the MountainCar environment -- Solution representation -- Evaluating the solution -- The Python problem representation -- Genetic algorithms solution -- Solving the CartPole environment -- Controlling the CartPole with a neural network -- Solution representation and evaluation -- The Python problem representation -- A genetic algorithm solution -- Summary -- Further reading -- Chapter 11: Natural Language Processing -- Technical requirements -- Understanding NLP -- Word embeddings -- Word embeddings and genetic algorithms -- Finding the mystery word using genetic algorithms -- Python implementation.

Document classification -- N-grams -- Selecting a subset of n-grams -- Using genetic algorithms to search for a fixed-size subset -- Python implementation -- Summary -- Further reading -- Chapter 12: Explainable AI, Causality, and Counterfactuals with Genetic Algorithms -- Technical requirements -- Unlocking the black box - XAI -- Unraveling cause and effect - causality in AI -- What-if scenarios - counterfactuals -- Genetic algorithms in counterfactual analysis - navigating alternative scenarios -- The German Credit Risk dataset -- Exploring counterfactual scenarios for credit risk prediction -- The Applicant class -- The CreditRiskData class -- Counterfactuals with genetic algorithms -- The genetic algorithm solution -- More "what-if" scenarios -- Extending to other datasets -- Summary -- Further reading -- Part 4: Enhancing Performance with Concurrency and Cloud Strategies -- Chapter 13: Accelerating Genetic Algorithms - the Power of Concurrency -- Technical requirements -- Long runtimes in real-world genetic algorithms -- Parallelizing genetic algorithms -- Multithreading -- Multiprocessing -- Back to the OneMax problem -- A baseline benchmark program -- Simulating computational intensity -- Multiprocessing using the Pool class -- Increasing the number of processes -- Multiprocessing using the SCOOP library -- Distributed computing with SCOOP -- Summary -- Further reading -- Chapter 14: Beyond Local Resources - Scaling Genetic Algorithms in the Cloud -- Technical requirements -- The next level in genetic algorithm performance -embracing a client-server architecture -- Implementing a client-server model -- Using a separate environment -- Revisiting the One-Max problem, yet again -- Creating the server component -- Creating the client component -- Running the asynchronous client -- Using a production-grade app server -- Using the Gunicorn server.

(MiAaPQ)EBC31497685

(Au-PeEL)EBL31497685

(OCoLC)1441721118