演化式計算

Introduce state-of-the-art evolutionary computation, use evolutionary systems as computational processes for solving complex engineering and scientific problems. Examine applications in IC design, artificial neural networks, machine learning, optimization, and evolutionary robotics/things. In additional to gaining understanding of applications in design and optimization, students will more importantly gain general understanding of EC as one of the foundational approaches to machine learning, soft computing, and global optimization/search, which has broad applicability across many domains including engineering computation, IoT, robotics, classification, adaptive agent learning, and neural networks.

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• • Object-oriented Python programming

• • Production Software Development Best Practices

• • Common EC Methods

  o Genetic Algorithms o Genetic Programming o Evolution Strategies

• Components of EC

  o Framework o Populations o Representation o Selection Operators o Genetic Operators

• EC Problem Solving

  o Search o Optimization o Machine Learning o Automated Programming o Adaptation

• • Python Multiprocessing

• • Parallel processing EC's

• • Multi-objective and constrained EC's

• • Multi-objective and constrained EC's

• • Advanced Application Topics

  o Evolutionary Electronics, Circuit Synthesis & Optimization o IC Layout & Floorplanning with EC o Clustering, Partitioning, Unsupervised Learning o Memetic/Hybridized EA's, Interactive EC o Artificial Neural Networks & Evolutionary Robotics/Things o Co-Evolutionary EA’s & Reinforcement Learning

• • History and fundamentals of Evolutionary Computation (EC)

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