George Yauneridge
Machine learning basics Types of learning algorithms Genetic algorithm basics Applications and the future of genetic algorithms
Machine learning is a topic of artificial intelligence The focus is on developing and implementing algorithms that allow machines to “learn” For machines, learning is acquiring new data and forming decisions based on all available data
Case Based Reasoning Modifies the solution from a past problem Decision Trees Analyze a tree of conditions to give yes/no answers Data Mining and Pattern Recognition Finds patterns in large amounts of data
Neural Networks Made of many units, each capable of input/output Reinforcement Learning The system analyzes its interactions with its environment, usually involving trial and error Inductive Logic Imitates human interpretation of data
Use DNA and evolution from biology as a model Use a population of parent solutions that compete to produce children Only the strongest solutions pass on their information
A each solution is called a chromosome Carries the information Several ways to encode the data Examples C1: C2:
“Strong” chromosomes are selected to pass on their information Strength of a chromosome is determined by a fitness function Imitates Darwin’s survival of the fittest theory
Chromosomes switch parts of their information to form children Several different methods C1: C2: Crossover C1: o100 C2:
Certain bits are inverted Imitates mutations that would occur in nature C1: Mutation C1:
Create a random population of chromosomes Evaluate the fitness of each chromosome Create the next population Select 2 fit parents Perform crossover Perform mutation Place the children in the new population Using the new population, test if the end condition is met Loop
minimum.php
Evolving music Strategy planning Evolving programs Other evolutionary and decision based applications
Association for the Advancement of Artificial Intelligence. AI Horizon. Marek Obitko. algorithms CMU CS Department. repository/ai/html/faqs/ai/genetic