Hands-On Neuroevolution with Python : Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms.: Build High-Performing Artificial Neural Network Architectures Using Neuroevolution-based Algorithms. (2019)

• Record Type: Book
• Other Names: Omelianenko, Iaroslav
• Contents: Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods -- Chapter 1: Overview of Neuroevolution Methods -- Evolutionary algorithms and neuroevolution-based methods -- Genetic operators -- Mutation operator -- Crossover operator -- Genome encoding schemes -- Direct genome encoding -- Indirect genome encoding -- Coevolution -- Modularity and hierarchy -- NEAT algorithm overview -- NEAT encoding scheme -- Structural mutations Crossover with an innovation number -- Speciation -- Hypercube-based NEAT -- Compositional Pattern Producing Networks -- Substrate configuration -- Evolving connective CPPNs and the HyperNEAT algorithm -- Evolvable-Substrate HyperNEAT -- Information patterns in the hypercube -- Quadtree as an effective information extractor -- ES-HyperNEAT algorithm -- Novelty Search optimization method -- Novelty Search and natural evolution -- Novelty metric -- Summary -- Further reading -- Chapter 2: Python Libraries and Environment Setup -- Suitable Python libraries for neuroevolution experiments NEAT-Python -- NEAT-Python usage example -- PyTorch NEAT -- PyTorch NEAT usage example -- MultiNEAT -- MultiNEAT usage example -- Deep Neuroevolution -- Comparing Python neuroevolution libraries -- Environment setup -- Pipenv -- Virtualenv -- Anaconda -- Summary -- Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems -- Chapter 3: Using NEAT for XOR Solver Optimization -- Technical requirements -- XOR problem basics -- The objective function for the XOR experiment -- Hyperparameter selection -- NEAT section -- DefaultStagnation section DefaultReproduction section -- DefaultSpeciesSet section -- DefaultGenome section -- XOR experiment hyperparameters -- Running the XOR experiment -- Environment setup -- XOR experiment source code -- Running the experiment and analyzing the results -- Exercises -- Summary -- Chapter 4: Pole-Balancing Experiments -- Technical requirements -- The single-pole balancing problem -- The equations of motion of the single-pole balancer -- State equations and control actions -- The interactions between the solver and the simulator -- Objective function for a single-pole balancing experiment Cart-pole apparatus simulation -- The simulation cycle -- Genome fitness evaluation -- The single-pole balancing experiment -- Hyperparameter selection -- Working environment setup -- The experiment runner implementation -- Function to evaluate the fitness of all genomes in the population -- The experiment runner function -- Running the single-pole balancing experiment -- Exercises -- The double-pole balancing problem -- The system state and equations of motion -- Reinforcement signal -- Initial conditions and state update -- Control actions -- Interactions between the solver and the simulator
• Publisher Details: Birmingham : Packt Publishing, Limited
• Publication Date: 2019
• Extent: 1 online resource (359 pages)
• Subjects: 006.3/2; Neural networks (Computer science); Python (Computer program language); Neural networks (Computer science); Python (Computer program language); Electronic books
• Languages: English
• ISBNs: 9781838822002; 1838822003
• Notes: Note: Print version record.
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