1. Tutorial 1 Temi avanzati di Intelligenza Artificiale - Lecture 3 Prof. Vincenzo Cutello Department of Mathematics and Computer Science University of Catania

2. Tutorial 1 - Lecture 32 Types of Evolutionary Algorithms 1) Genetic Algorithms (GAs)  Proposed and studied by John Holland and his students  Numerical optimization and adaptive systems design Simulate Darwinian Evolution Recombination (Crossover) important Mainly binary representation Schema Theorem classical GA theory

3. Tutorial 1 - Lecture 33 Types of Evolutionary Algorithms 2) Genetic Programming (GP)  Developed by Koza et al.  Evolved LISP programs Tree Representation Widest variety of application  Term also used by De Garis for evolution of artificial neural networks

4. Tutorial 1 - Lecture 34 Types of Evolutionary Algorithms 3) Evolutionary Programming (EP)  Developed by Fogel for simulated intelligence  Finite State Machine (FSM) representation close to Lamarckian inheritance No recombination Adaptive Mutation (and others) Applied to Phenotypes

5. Tutorial 1 - Lecture 35 Types of Evolutionary Algorithms 4) Evolution Strategies  Introduced by Rechenberg and Schwefel for numerical optimzation  Real-valued representation Mutation Based Adaptive Mutation

6. Tutorial 1 - Lecture 36 Search Operators and Bias 1) Mutation Bias  Example: Integer variables in binary representation 2 variables, each 3 bit integer - 6 bit genotype Current value: ( ) Single-bit bitflip mutation Where can we go in one mutation? How many mutations are required to get to ?

7. Tutorial 1 - Lecture 37 Search Operators and Bias Crossover Bias  One-Point vs. Uniform Crossover  Example Representation: Strings of integer values (0 - m) of length n  Two fitness functions: 1: Fitness = 2: Fitness = Minimzation problem  Suggestions ?

8. Tutorial 1 - Lecture 38 Search Operators and Bias Crossover Bias  Example System m=4 n=200 Population size: 400 Mutation: single-gene random raplacement, 1.0 per- individual mutation probability Crossover: uniform or one-point crossover, 1.0 crossover probability Replacement: keep best Termination: when fitness == 0

9. Tutorial 1 - Lecture 39 Selection Pressure  Different selection operators produce different behaviour  Exploration vs. Exploitation  Example: Roulette Wheel and Tournament selection

10. Tutorial 1 - Lecture 310 Selection Pressure Example Case  Two-optima function Genotype: real-valued vector, -10.0 to 10.0 Mutation: gaussian mutation, 1.0 probability, 0.7 deviation; Crossover: none Population size: 20, Replacement: keep best Maximization problem

11. Tutorial 1 - Lecture 311 Excercise sheet  1. Write an evolutionary algorithm...... to find the maximum of the function on slide 10: f(x,y) = e (- 0.7 * (x+2.0) *(x+2.0)) * e(-0.9 * y * y) + 2.0 * e (- (x -5.0) * (x-6.0)) * e (-(y-2.0) * (y-2.0)) For values -10.0 < x,y < 10.0 Two versions...  Version one: binary representation (e.g. 2 x 16 bit)  Version two: real-value representation (see next week's lectures) Describe the differences  Run a few runs, and write a short (1/2 page) description of the differences in search behaviour between the two representations. E.g. how long does it need to find the optimum? Does it always find the global optimum? How close to the optimum does it get?  2. Think about... If you have a genotype of length n, what is the difference between n-1 point crossover and uniform crossover ? Describe the difference in a few lines.