Prof. (Dr.) Hardeep S Rai
Guru Nanak Dev Engg College, Ludhiana
Let us start with an interesting demo. See how car's design is being evolved generation over generation, using Genetic Algorithm:
To come back to this page, after seeing demo (from link below), you may use
a sub-field of AI
so what is ML?
It is buzz word now a day!
Is it a new word?
What do you think?
How old is this term?
Term coined in 1959.
by Arthur Lee Samuel
Samuel Checkers-playing Program
among the world's first successful self-learning programs
senior member in the TeX community
Andrew Yan-Tak Ng is a Chinese American computer scientist.
Was chief scientist at Baidu
is adjunct professor at Stanford University.
Co-founder and chairman of Coursera.
Was Director of the Stanford Artificial Intelligence Lab.
Taught and undertook research related to data mining and machine learning.
Worked at Google (2011-12), founded and led the Google Brain Deep Learning Project.
Offered free online courses for everyone after over 1 Lac students registered for course on "Machine Learning. Today, several million people have taken the course.
At Baidu, carried out research related to big data and A.I.
Launched Deeplearning.ai, an online curriculum of classes.
Launched Landing.ai, bringing AI to manufacturing factories.
In 2018, Ng unveiled the AI Fund, raising $175 million to invest in new startups.
Chairman of Woebot and on the board of drive.ai.
Stanford Autonomous Helicopter project, which developed one of the most capable autonomous helicopters in the world
STAIR (STanford Artificial Intelligence Robot) project, which resulted in ROS, a widely used open-source robotics software platform.
Google Brain project, very large scale ANN
NN trained using deep learning on 16k CPU cores, learned to recognise higher-level concepts (cats) after watching only YouTube videos.
Currently used in the Android OS's speech recognition system.
In ML we create solutions by using data or examples. There are 2 ways to do this.
GAs are stochastic search algorithms which are often used in ML applications.
This distinction might not be strict; ID3 (Iterative Dichotomiser3) might include a search over different prunings, for example.
Nonetheless, algorithms like ID3 are fast and computationally simple.
In addition, there is usually an explicit simplifying assumption about the nature of the solutions. For example, in ID3 the bias is towards independent attributes;
Using search, however, a wide range of complex potential solutions can be tested against the examples, and thus, much more difficult problems can be tackled. The cost, of course, is in increased computational cost.
In addition, any inductive bias is embedded in the algorithm and is thereby more difficult to control, although introducing explicit controls or regularisers is becoming increasingly important.
In many respects, genetic algorithms are on the dumb and uncontrolled end of machine learning methods. They rely least on information and assumptions, and most on blind search to find solutions. However, they have been seen to be very effective in a range of problems.
Genetic algorithms are important in machine learning for three reasons.
Are essentially reinforcement learning algorithms. The performance of a learning system is determined by a single number, the fitness.
GAs involve a population. Learning in multi-agent systems.
Professor of psychology
Professor of electrical engineering and computer science
@ University of Michigan, Ann Arbor.
Pioneer in GAs, Father of Genetic Algorithm
Researched in "complex adaptive systems".
Ground-breaking book on GAs, "Adaptation in Natural and Artificial Systems" (1975).
Developed Holland's schema theorem.
Hidden Order: How Adaptation Builds Complexity (1995)
Emergence: From Chaos to Order (1998)
Signals and Boundaries: Building Blocks for Complex Adaptive Systems (2012)
Complexity: A Very Short Introduction (2014)
Computer scientist, Civil engineer
Professor at the department of Industrial and Enterprise Systems Engineering (IESE) at the University of Illinois at Urbana-Champaign
Most noted for his work in the field of GAs.
Director of the Illinois Genetic Algorithms Laboratory (IlliGAL)
Chief scientist of Nextumi Inc.
He is the author of Genetic Algorithms in Search, Optimization and Machine Learning, one of the most cited books in computer science.
are search algorithms based on the mechanics of natral selection and natural genetics
combine Darwinian survival of the fittest with innovative flair.
new set of creatures is generated using bits and pieces of the fittest of the old, an occasional new part is tried.
randomised, but !(random walk).
efficiently exploit historical information to speculate new search points with expected improved performance.
a simple simulation by hand.
For selection by roulette wheel, click here.
The steps of applying GA are:
Choose an encoding
Choose a fitness funtion
Choose initialisation and stopping criteria
Selection (biased roulette wheel, tournament selection, elitism selection)