'Random population' presentation slideshows

Dynamical Systems Model of the Simple Genetic Algorithm

674 views • 67 slides

Evolutionary Computation

Evolutionary Computation. 22c: 145, Chapter 9. What is Evolutionary Computation?. A technique borrowed from the theory of biological evolution that is used to create optimization procedures or methodologies, usually implemented on computers, that are used to solve problems.

1.46k views • 64 slides

Lecture 18: Association Studies I

Lecture 18: Association Studies I. Date: 10/24/02 A mathematical formalism for linkage disequilibrium Allelic association in random populations Allelic association in case-control populations. Where We Are. We will be covering chapter 4 of Sham.

570 views • 47 slides

SOFT COMPUTING Evolutionary Computing

SOFT COMPUTING Evolutionary Computing. EC. What is a GA?. GAs are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. As such they represent an intelligent exploitation of a random search used to solve optimization problems.

697 views • 45 slides

Coalescent theory

Coalescent theory. Expectation, and deviance. Statements such as the ones below can be made only if we have an underlying model that suggests what we should expect. Recombination rates vary dramatically across the genome There was a population bottleneck in Iceland

637 views • 44 slides

Coalescent theory

Coalescent theory. Expectation, and deviance. Statements such as the ones below can be made only if we have an underlying model that suggests what we should expect. Recombination rates vary dramatically across the genome There was a population bottleneck in Iceland

715 views • 44 slides

Biologically Inspired AI (mostly GAs)

Biologically Inspired AI (mostly GAs). Some Examples of Biologically Inspired Computation. Neural networks Evolutionary computation (e.g., genetic algorithms) Immune-system-inspired computer/network security Ant-colony optimization Swarm intelligence (e.g., decentralized robots)

325 views • 18 slides

Evolving the goal priorities of autonomous agents

Evolving the goal priorities of autonomous agents. Adam Campbell Advisor: Dr. Annie S. Wu School of Electrical Engineering and Computer Science. Outline. Motivation Genetic algorithms Agent control system Experiments and results Conclusion and future work. Motivation.

239 views • 14 slides

The authors have no financial interest in the subject matter of this e-poster

388 views • 12 slides

BME 1300 Problem 1 Newstetter Group

BME 1300 Problem 1 Newstetter Group. Names of Group. Problem.

218 views • 11 slides