evolution & genetic algorithms
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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. - PowerPoint PPT PresentationTRANSCRIPT
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