Advanced AI – Session 6 Genetic Algorithm By: H.Nematzadeh

Objectives To understand the processes involved ie. GAs Basic flows –operator and parameters (roles, effects etc) To be able to apply GAs in solving optimisation problems

Evolutionary computation we are products of evolution, and thus by modelling the process of evolution, we might expect to create intelligent behaviour. Evolutionary computation simulates evolution on a computer. The result of such a simulation is a series of optimisation algorithms, usually based on a simple set of rules. Optimisation iteratively improves the quality of solutions until an optimal, or at least feasible, solution is found.

Nature like evolution is slow Evolution is a tortuously slow process from the human perspective, but the simulation of evolution on a computer does not take billions of years!

Natural evolution Evolution can be seen as a process leading to the maintenance of a population’s ability to survive and reproduce in a specific environment. This ability is called evolutionary fitness. Evolutionary fitness can also be viewed as a measure of organism’s ability to anticipate changes in its environment. The better an organism's fitness to the environment, the better its chances to survive

Rabbits & Foxes

Encoding Vs Evaluation

Class of searches techniques

Evolutionary Process

Mice & Cats: an evolutionary problem

The mice & cat algorithm

General evolution process

GA Vs Real life

Basic GA

Another way of looking at this…

Flowchart of GA

Another way of looking at this…

GA Process

Example 1 (not included in the book) burger and profit problem

Analysis

Fitness Evaluation

Selection

Crossover

Mutation

After 1 st run

Example 2: optimization of a one variable function

Steps in GA development

The entire universe of discourse

Operator parameters

Fitness function

The fitness functions and chromosomes location

Selection using roulette wheel One of the most commonly used chromosome selection techniques is the roulette wheel selection (Goldberg, 1989; Davis, 1991). Figure 7.4 illustrates the roulette wheel for our example. As you can see, each chromosome is given a slice of a circular roulette wheel.

Selection using roulette wheel

Crossover function

Mutation function

GA cycle

Example 3- 2 variables function