icAlgorithms Optimization Method 最优化方法经典教材课件[精].ppt

Basics of ic Algorithmsand some possibilities
Peter Spijker
Technische Universiteit Eindhoven
Department of Biomedical Engineering
Division of Biomedical Imaging and Modeling
California Institute of Technology
Materials Process and Simulation Center
Biochemistry & Molecular Biophysics
November 25, 2003
12
Presentation Overview
Purpose of presentation
General introduction to ic Algorithms (GA’s)
Biological background
Origin of species
Natural selection
ic Algorithm
Search space
Basic algorithm
Coding
Methods
Examples
Possibilities
Purpose of presentation
Optimising parameters of force fields is a difficult and time consuming task
Use of optimising methods might be of use
Methods:
steepest descent
simulated annealing (Monte Carlo)
ic algorithms
Brief introduction to ic algorithms in lecture style
General Introduction to GA’s
ic algorithms (GA’s) are a technique to solve problems which need optimization
GA’s are a subclass of puting
GA’s are based on Darwin’s theory of evolution
History of GA’s
puting evolved in the 1960’s.
GA’s were created by John Holland in the mid-70’s.
Biological Background (1) – The cell
Every animal cell is plex of many small “factories” working together
The center of this all is the cell nucleus
The nucleus contains the ic information
Biological Background (3) – ics
The bination of genes is called genotype
A genotype develops to a phenotype
Alleles can be either dominant or recessive
Dominant alleles will always express from the genotype to the fenotype
Recessive alleles can survive in the population for many generations, without being expressed.
Biological Background (4) – Reproduction
Reproduction of ical information
Mitosis
Meiosis
Mitosis is copying the same ic information to new offspring: there is no exchange of information
Mitosis is the normal way of growing of multicell structures, ans.
Biological Background (5) – Reproduction
Meiosis is the basis of sexual reproduction
After m