Evolution & Genetic Algorithms

Evolution & Genetic Algorithms

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.

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.

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

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.

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

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

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

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.

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

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

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

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

Selecting a Mate:

Parents Selection Techniques: Explicit fitness remapping

Fitness scaling Fitness windowing Fitness ranking

Implicit fitness remapping Use tournaments to choose parents

Crossover Reproduction

1-point crossover: Two mating chromosomes are cut at one point

and the cuts are exchanged between the two parents.

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

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

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

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

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

Applications for Genetic Algorithms

Various medical applications, such as image segmentation and modeling.

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