ECE
032607

# 032607 - Lecture 16 Recall Standard(Simple Genetic...

• Notes
• 501844684_ch
• 3
• 100% (2) 2 out of 2 people found this document helpful

This preview shows pages 1–2. Sign up to view the full content.

Lecture 16: 03/26/2007 Recall: Standard (Simple) Genetic Algorithm (SGA) Fixed-size populations (n) Individuals in population are fixed-length (L) bit strings Exogenous fitness function—each bit string x has a fitness f(x)—assume that f(x)>0, all x P(t)-->P(t+1) (populations) [that is P(t) evolves to P(t+1)] via: 1. Selection of parents for P(t+1) from P(t) 2. Generation of offspring from selected parents Talked briefly about various selection methods (fitness-prop, tournament, ranked, elitist.) Generation of offspring via crossover mutation . Crossover probability p c and mutation probability p m —p c ~.75, p m ~.01 and also different kinds of crossover (one-point, two-point, or uniform.) Do some back-of-envelope calculation regarding SGA with fitness-proportional selection with one-pt. crossover (w/ prob. P c ), bitwise mutation (p m ). What is fitness-prop. selection ? A probabilistic selection method that gives every individual a non-zero probability of being selected as a parent (ie. don’t just pick the best!) and doesn’t guarantee any individual is a parent. To get the n-parents, draw from P9t); draw an individual xєP sel (x) proportional to f(x) with replacement.

This preview has intentionally blurred sections. Sign up to view the full version.

This is the end of the preview. Sign up to access the rest of the document.
• Spring '07
• DELCHAMPS
• Algorithms, SGA, average fitness, fitness proportional selection, H Holland, highly fit schemas

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern