文档介绍：Genetic Algorithms and Optimization
H. Peng
School of Mathematics and Computer Engineering,
Xihua University
Introducing Genetic Algorithms
V. Hybrid Genetic Algorithms
II. Examples with Simple Genetic Algorithms
I. Introduction
III. Encoding
IV. Genetic Operators
VI. Adaptation of Genetic Algorithms
Genetic Algorithms (GA):
Evolution Strategies (ES):
Evolutionary Programming (EP):
Genetic Programming (GP):
Holland, J. - Adaptation in Natural and Artificial Systems, University of
Michigan Press, 1975 and MIT Press, 1992.
Rechenberg, I. - Evolutionsstrategie: Optimierung technischer Systeme nach
Prinzipien der biologischen Evolution, Frommann-Holzboog, 1973.
Schwefel, H. - Evolution and Optimum Seeking, John Wiley & Sons, 1995.
Fogel, L., A. Owens, and M. Walsh - Artificial Intelligence through Simulated
Evolution, John Wiley & Sons, 1966.
Koza, J. R. - Genetic Programming, MIT Press, 1992;
Genetic Programming II, MIT Press, 1994.
Evolutionary Computation (EC) Methods
I、Introduction
What is Genetic Algorithms ?
the most widely known type of evolutionary computation;
powerful and broadly applicable stochastic search and optimization
techniques.
The Features of Genetic Algorithms
- Working with a coding of solution set, not the solutions
themselves.
- Searching from a population of solutions, not a single
solution.
- Using payoff information (fitness function), not
derivatives or other auxiliary knowledge.
- Using probabilistic transition rules, not deterministic
rules.
What is the Essence of Genetic Algorithms?
Random Search + Directed Search
f (x)
0
x
max f (x)
. 0 x ub
Five Basic Components
- A genetic representation (chromosome or individual ) of
potential solutions to the problem.