1. A simple EA and Common Search Operators Temi avanzati di Intelligenza Artificiale - Lecture 2 Prof. Vincenzo Cutello Department of Mathematics and Computer Science University of Catania

2. A simple EA and Common Search Operators - Lecture 2 2 Evolutionary Computation ...is the study of computational systems which use ideas and get inspirations from natural evolution and other biological systems  Evolutionary Algorithms (EAs) are closely linked to AI techniques, especially search techniques. EAs can be regarded as population-based stochastic generate-and-test algorithms.  Evolutionary Computation (EC) techniques are used in optimization, machine learning and automatic design.

3. A simple EA and Common Search Operators - Lecture 2 3 A simple Evolutionary Algorithm  1 Generate initial Population P(0) at random, and set i=0;  2 repeat 3 Evaluate the fitness of each individual in P(i) 4 Select parents from P(i) based on their fitness in P(i) 5 Apply crossover to create offspring from parents 6 Apply mutation to the offspring 7 Select generation P(i+1) from current offspring O(i) and parents P(i)  8 until finished

4. A simple EA and Common Search Operators - Lecture 2 4 Example (Part 1)  Example Problem Use EA to find the maximum of an unknown function f(x,y) With x,y integers between 0 and 8  Preparation: Decide Encoding Binary Encoding 6 bit genotype length  Step 1: Create initial Population Randomly assemble binary strings of length 6  Step 2: Enter loop

5. A simple EA and Common Search Operators - Lecture 2 5 Example (part 2):  Step 3: Calculate Fitness Decode genotypes into integer values Compute fitness value from objective function  Step 4: Select n Parents Randomly select m members of P(i) (e.g. m=2) The best of these is the first parent Repeat until all parents selected (so-called "Tournament Selection")

6. A simple EA and Common Search Operators - Lecture 2 6 Example (part 3)  Step 5: Perform Crossover For each pair of 2 parents: Select a random crossover point Create two offspring from genotype segments of parents (so-called 'One-point Crossover')  Step 6: Perform Mutation For each offspring: Randomly select one bit Flip this bit (so-called 'Bitflip Mutation')

7. A simple EA and Common Search Operators - Lecture 2 7 Example (part 4)  Step 7: Select next generation E.g. keep the best 6 from current generation and offspring  Step 8: Continue loop or terminate E.g. Terminate after n loops without change in the population (So-called convergence)

8. A simple EA and Common Search Operators - Lecture 2 8 Remarks  There is no restriction on the fitness (objective) function. It can be non-differentiable or even discontinuous  There is no need to know the exact form of the objective function. Simulation can be used to derive a fitness value  The initial population does not have to be generated randomly You can use existing knowledge to seed population  The representation does not have to be binary  There are alternative options for all steps of the algorithm Genetic operators, selection, termination

9. A simple EA and Common Search Operators - Lecture 2 9 Recombination / Crossover Operators  One-Point Crossover  K-Point Crossover  Uniform Crossover  Crossover rate: the probability of applying crossover  Other operators for nonlinear representations

10. A simple EA and Common Search Operators - Lecture 2 10 Mutation Operators  Bit-Flipping  Random bit assignment  Multiple bit mutations  Per-chromosome mutation rate vs. per-gene (bit) mutation rate  Other operators for non-binary representations

11. A simple EA and Common Search Operators - Lecture 2 11 Selection  Roulette wheel selection  Fitness proportional selection  Rank-based selection  Tournament selection  More complex operators

12. A simple EA and Common Search Operators - Lecture 2 12 Search Bias  Some offspring tend to be more likely to be generated than others. This is called a bias  Depends on representation and operators Crossover bias, e.g. one-point vs. uniform Mutation bias, e.g. 1-bit-flip vs. k-bit-flip

13. A simple EA and Common Search Operators - Lecture 2 13 Sumary  EC is a field of study that includes EAs and other areas. EAs include many different types of algorithms.  Search Operators generate new offspring from parents. There is no limiation on what operators can be used.  Search operators are applied to individuals. It is very important to realize the interdependency between operators and the representation of individuals.

14. A simple EA and Common Search Operators - Lecture 2 14 Recommended Books TitleAuthor(s)PublisherComments Handbook on Evolutionary Computation T. Baeck, D. B. Fogel, and Z. Michalewicz (eds.) IOP Press, 1997.Very good reference to evolutionary computation. Should read relevant sections after each lecture. Genetic Algorithms + Data Structures = Evolution Programs (3rd edition) Z MichalewiczSpringer-Verlag, Berlin, 1996 Recommended reference book for this module. It is more up-to-date than Goldberg's book. Genetic Algorithms in Search, Optimisation & Machine Learning D E Goldberg Addison-Wesley, 1989 Good introductory book on genetic algorithms and classifier systems, but no other topics. Somewhat out of date. Genetic Programming: An Introduction W Banzhaf, P Nordin, R E Keller & Frank D Francone Morgan Kaufmann, 1999 A good introductory book on genetic programming. Evolutionary Computation: Theory and Applications X. Yao (ed)World Scientific Publ. Co., Singapore, 1999. Good reference for more advanced topics. Various articles in journals and conference proceedings A list of papers will be specified as the module progresses.

15. A simple EA and Common Search Operators - Lecture 2 15 References and Resources for this Lecture  Books (see previous page)  Web Resources EvoNet Flying Circus Lots of material on evolutionary computation EvoNet Flying Circus The Hitch-Hiker's Guide to Evolutionary Computation The Hitch-Hiker's Guide to Evolutionary Computation Introduction to evolutionary computation Ricardo Poli's lecture notes for this course Introduction to evolutionary computation