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A formal presentation of GA’s
“ The Gene is by far the most sophisticated program around. ”
When to use them
- Alternate solutions are too slow or overly complicated
- Need an exploratory tool to examine new approaches
- Problem is similar to one that has already been successfully solved by using a GA
- A quasioptimal solution is allowed
When no to use them
- If you need the optimal solution
- If you have the Analytical solution or it can be easily solved (one variable functions)
- If the solution is a convex function
- If the space is discrete and enumerable
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.
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
Genetic Operators
- Encoding Method of representation of the coefficient in a gene Note that if there is no encoding then we talk about evolutionary programming
- Mutation When a bit inside the chromosome is invested (extremely rare)
- Internal positional swap When two bit inside the chromosome exchanges their position (arity=1)
- Reproduction When a new population is generated
- Crossover (aka recombination) When randomly two chromosomes are chosen bits are crossed over generating two new individuals (arity = 2)
- Substitution When the parents of a crossover are eliminated from the population
- Decoding Process of obtaining of the value for a coefficient
GA operators: Selection
- Main idea: better individuals get higher chance
- Chances proportional to fitness
- Implementation: roulette wheel technique » Assign to each individual a part of the roulette wheel » Spin the wheel n times to select n individuals
7
fitness(A) = 3
fitness(B) = 1
fitness(C) = 2
A (^) C
1/6 = 17%
3/6 = 50%
B
2/6 = 33%
Initial Population
Although the initial population is assumed random you may
consider the following options
If possible start with some range values for the function coefficients to faster convergence. However remember that this might work like the Crash Cut of Minimax
Similarly you can start with some given alleles or seed values. For example use the values you obtain from your own experience.
- Diversity control to avoid Inbreading and Locking
If you use the previous considerations remember to add some diversity either by mutation or in the evaluation function to allow to step out of the box you have predefined
Chromosomes in time
To determine which chromosomes are selected in next
generation may include aspects such as:
- Diversity
- Parallelism
- Elitism
- Specialization Niches
- Meta Chromosomes
- Substitution rather than Addition
- Deception
- Always plots
- Best in generation
- Generation as a whole (^11)
Best in the generation
Generation performance
On Natural Genetics
- ALL the proteins of the live beings in our planet are composed of sequences of 20 amino acid
- The DNA that forms the chromosomes is built by four nucleotidos in the shape of double helix: - Purines: Adenosine, Guanine - Pyrimidines: Thiamine (or Uracil), Citosine
- The code is TEtranary digiT (TET) = {A,G,T,C}
- Codons are triplets of nucleotides (ATC, GTC,…) just as bytes related to 8 bits. Yet there are only 20 aminoacids so there is great amount of redundancy in the codes.
- Furthermore the DNA contains a high percentage of chains that are not used, yet help in schemata resolutions.
- The DNA is used in the three living species:
- Plants, Animals and Fungi
Schemata Analysis
- It is intuitive to notice that the DL of a given H plays an important
part in the probability of destruction or survival of the H through the time.
- The longer the string that defines H the greater its probability of extinction in the next generations
- For this reason an important hypothesis of GA’s is that the
construction blocks of a genes in a chromosome should be small
- The fundamental Theorem of the AG tells us that:
- Schemata of low order, with reasonable adaptation, will have an exponential growth in the number of its instances in subsequent generations of chromosomes
Softcomputing
Softcomputing refers to a set of problem solving methods
used when the required solution
- Permits some degree of imprecision
- It is subject to uncertainty
- Deals with knowledge(beliefs, choices) rather than information(truth)
- A quasi optimized solution is acceptable
- Allows ample time to come with the solution
In this case soft computing such as GA’s offers solutions with
- Robust results
- Low cost
- Easy tractability
Sources and References
- Thais Melo URL : http://www.cs.princeton.edu/~tmelo/pres.ppt
- Gerardo Mendoza and Dan Reich URL: http://cow.math.temple.edu/~cow/cgi- bin/manager
- Jim Cohoon and Kimberly Hanks URL: http://www.cs.virginia.edu/~evans/bio/slides/ga1.ppt
- Jennifer Pittman URL: http://www.niss.org/affiliates/proteomics200303/ presentations20030306/04%20Jennifer.ppt
- Peter Cowling, Graham Kendall, Limin Han + URL: http://www.cs.nott.ac.uk/~lxh/cec2.pdf
- Eyal Allweil and Ami Blonder URL: http://www.cs.huji.ac.il/course/2002/aisemin/talks/week8a.ppt
- Noyan Turkkan URL: http://www.umoncton.ca/turk/Genetik201.xls
- EfraimTurban & Jay E. Aronson URL: http://mgtclass.mgt.unm.edu/Bose/MGT%20539/
- Hitch Hiker URL: http://www.cs.bham.ac.uk/Mirrors/ftp.de.uu.net/EC/clife/www/Q99_E.htm
- Jim Smith, Agoston Eiben & Jano van Hemert http://www.evonet.polytechnique.fr/evoweb/resources/flying_circus/slides/
