This document discusses genetic algorithms and their applications. It explains key concepts like genetic crossover, genetic algorithm steps to solve optimization problems, and how genetic algorithms mimic biological evolution. Examples are provided of genetic algorithms being used for tasks like predicting protein structure, automotive design optimization, and generating musical variations. Advantages and limitations of genetic algorithms are also summarized.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
Performance of genetic algorithm is flexible enough to make it applicable to a wide range of problems, such as the problem of placing N queens on N by N chessboard in order that no two queens can attack each other which is known as ‘n-Queens problem.
Lack of information about details of the problem made genetic algorithm confused in searching state space of the problem
When automation is the hype we need to focus on solving ever complex problems. The key of solving such large and complex task is on cooperation, not in monolithic solutions that require too much resources to run.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
GDC2019 - SEED - Towards Deep Generative Models in Game DevelopmentElectronic Arts / DICE
Deep learning is becoming ubiquitous in Machine Learning (ML) research, and it's also finding its place in industry-related applications. Specifically, deep generative models have proven incredibly useful at generating and remixing realistic content from scratch, making themselves a very appealing technology in the field of AI-enhanced content authoring. As part of this year's Machine Learning Tutorial at the Game Developers Conference 2019 (GDC), Jorge Del Val from SEED will cover in an accessible manner the fundamentals of deep generative modeling, including some common algorithms and architectures. He will also discuss applications to game development and explore some recent advances in the field.
The attendee will gain basic understanding of the fundamentals of generative models and how to implement them. Also, attendees will grasp potential applications in the field of game development to inspire their work and companies. This talk does not require a mathematical or machine learning background, although previous knowledge on either of those is beneficial.
2. Survival of the Fittest
Natural selection
Sir Charles Darwin
3. Chromosomes from two
different parents
Chromatids from each
overlap at Chiasma
Recombinant chromosomes
are form
Further passed on to the
progeny
Genetic Crossover
4. A T T G C T C ORIGINAL
A T A G C T C SUBSTITUTION
A T T G A C T C ADDITION
A T G C T C DELETION
5. Offsprings have
combinations of features
inherited from each
parent
Image adapted from http://www.wpdipart.com
7. Genetic Algorithm is a type of local search that
mimics evolution by taking a population of strings
which encode possible solutions and combines them
based on a fitness function to produce individuals that
are more fit.
8. 1) Encoding the two numbers into binary strings
Parent 1=3.273672 =>11.0100011000001
Parent 2=3.173294 =>11.0010110001011
2) Randomly choose a crossover point; let suppose be it at bit 6,
and we split the gene at position six.
Parent 1=>3.273672=>11.010---0011000001
Parent 2=>3.173294=>11.001---0110001011
3) Swapping the two tails ends of binary strings.
Child 1=>11.010---0110001011
Child 2=>11.001---0011000001
4) Recombining the two binary strings to get two new offspring.
Child 1=>11.0100110001011
Child 2=>11.0010011000001
5) Decoding the binary strings back into floating point numbers.
Child 1=3.298218
Child 2=3.148560
25. ADVANTAGES LIMITATIONS
No training required Do not work well when the
population size is small and
the rate of change is too high.
Efficient even during
Multi-modal or
n-dimensional search space
If the fitness function is chosen
poorly or defined vaguely, the
Can work for non-linear Genetic Algorithm may be
equations too unable to find a solution to the
problem, or may end up
Efficient solving the wrong problem
26. GAOT- Genetic Algorithm Optimization
Toolbox in Matlab
JGAP is a Genetic Algorithms and Genetic
Programming component provided as a Java
framework
Generator is another popular and powerful
software running on Microsoft Excel
27. Genetic Algorithm is related to “solving
problems of everyday interest” in many
diverse fields.
However, several improvements can be
made in order that Genetic Algorithm could
be more generally applicable. Future work
will continue through evolution and many
more specific tasks
28. Introduction to Genetic Algorithms -Axcelis
http://www.axcelis.com:80/articles/itga/application.html
How Genetic Algorithm works
http://www.mathworks.in/help/toolbox/gads/f6187.html
Introduction to Bioinformatics
By Sundararajan & R. Balaji
Functioning of a Genetic Algorithm
http://www.rennard.org/alife/english/gavintrgb.html#gafunct