Evolution & Genetic Algorithms

1. Evolution & Genetic Algorithms

2. Lamarckian Evolution • Lamarckian Theory • Based on the concept of use and disuse • Over a few generations, a given structure or organ will increase in size if the creature and its parents use that structure often. • On the other hand, if a structure and organ is in disuse it will get smaller and even disappear in subsequent generations.

3. An Example of Lamarckian Evolution • A Giraffe has a long neck because its ancestors used its neck to reach food. • Based on Lamarck’s theory, the Giraffe of the future will have an even longer neck than its contemporary relatives.

4. Darwinian Evolution… • All animals are constantly changing and evolving • The primary goal of an animal is to mate and have as many offspring as possible • Concept of natural/sexual selection • Natural selection, development, and evolution requires time

5. Darwin’s Evolution… • A creatures survivability is not the result of divine intervention or due to a desire to seek perfection. • It is through the process of natural selection that creatures evolve into what they are now.

6. Biological Evolution… • Evolution refers to the cumulative changes that occur in a population • Biological evolution is not a random process. • It is a constantly occurring phenomenon • Genes are the key components in the process of evolution. • Any physical characteristics acquired during the organisms life are not transferred to their

7. Biological Evolution And Genetic Algorithms • Biological Evolution is the inspiration for genetic algorithms • Most of the principles associated with biological evolution also apply to genetic algorithms • Unlike evolution, genetic algorithms will stop after a finite number of gnerations

8. What Are Genetic Algorithms • They are essentially search algorithms • Given a large search space, GA’s will evolve to the correct solution to a problem over a series of generations. • GA’s do not guarantee an optimal solution to a problem ie. Traveling salesman problem

9. What are Genetic Algorithms continued… • Genetic Algorithms are useful at finding “acceptably good solutions… acceptably quickly” • Nevertheless, if an optimized strategy already exists for a given problem, it is best to use it rather than a GA.

10. Components of a Genetic Algorithm • The population of potential solutions • A fitness function • A process for selecting mating pairs and introducing their offspring into the original population

11. Coding a Genetic Aglorithm • First consider the parameters of the problem • Use binary numbers to represent each parameter • Other’s have suggested using a user defined language to encode the problem • Once the parameters are established, generate a random initial population

12. Fitness Fuction • It is analogous to the environment an animal lives in • Gives a numerical description of how fit the solution encoded in a particular chromosome is. • Penalty Functions • Approximate Function evaluation

13. Issues With Fitness Functions • Premature convergence • When a super fit (although not optimal) chromosome dominates the population • This chromosome usually represents a local maximum • Makes it impossible to use fitness alone as an indicator of reproductive potential • Slow finishing • When the populations have a high average fitness and don’t have the extra oomph to push further and find a maximum

14. Selecting a Mate: • Parents Selection Techniques: • Explicit fitness remapping • Fitness scaling • Fitness windowing • Fitness ranking • Implicit fitness remapping • Use tournaments to choose parents

15. Crossover Reproduction • 1-point crossover: • Two mating chromosomes are cut at one point and the cuts are exchanged between the two parents.

16. Cross Over Reproduction… • 2-point crossover: • Instead of a linear string, think of the chromosome as a loop formed by joining both ends. • To mate, just cut a section in both parent loops and exchange missing sections • Is preferred over 1-point crossover because it allows one to search the problem space more thoroughly

17. Crossover Reproduction… • Uniform crossover: • A randomly generated cross over mask is created for each pair of parents. • Based on the mask, the parents copy their genes to create new offspring. • Where there is a 1, parent 1 copies its gene • Where there is a 0, parent 2 copies its gene

18. Introducing Offspring Into the Population • In most genetic algorithm examples, the whole population is replaced with the offspring • The generation gap is 1 • In the insect world, parents die soon after the eggs are laid

19. Introducing Offspring Into the Population • Steady-state • Inspired by mammals and other long lived creatures. • The offspring must compete with themselves and with their parents • The steady-state technique require that an unlucky group of parents must die off to make room for the offspring

20. Steady-State case… • Possible methods for choosing which parents will meet their demise: • Select parents according to fitness, and select random offspring to replace them. • Select parents at random, and use fitness to choose offspring. • Select both according to their fitness

21. Applications for Genetic Algorithms • Various medical applications, such as image segmentation and modeling.

22. Robotic Applications… • Genetic Algorithms can be used to teach robots how to move. • Brandeis University made a robot mother who created offspring using genetic algorithms • One of her offspring is shown in the picture